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RecSys Tables of Contents: 070809101112131415

Proceedings of the 2007 ACM Conference on Recommender Systems

Fullname:Proceedings of the ACM Conference on Recommender Systems
Editors:Joseph A. Konstan; John Riedl; Barry Smyth
Location:Minneapolis, Minnesota
Dates:2007-Oct-19 to 2007-Oct-20
Publisher:ACM
Standard No:ISBN: 1-59593-730-7, 978-1-59593-730-8; ACM DL: Table of Contents hcibib: RecSys07
Papers:38
Pages:212
Links:Conference Home Page
  1. Privacy and trust
  2. Algorithms: collaborative filtering
  3. User issues in recommender systems
  4. Algorithms: learning
  5. Research short papers
  6. Practice/industry track abstracts
  7. Doctoral symposium

Privacy and trust

Private distributed collaborative filtering using estimated concordance measures BIBAFull-Text 1-8
  Neal Lathia; Stephen Hailes; Licia Capra
Collaborative filtering has become an established method to measure users' similarity and to make predictions about their interests. However, prediction accuracy comes at the cost of user's privacy: in order to derive accurate similarity measures, users are required to share their rating history with each other. In this work we propose a new measure of similarity, which achieves comparable prediction accuracy to the Pearson correlation coefficient, and that can successfully be estimated without breaking users' privacy. This novel method works by estimating the number of concordant, discordant and tied pairs of ratings between two users with respect to a shared random set of ratings. In doing so, neither the items rated nor the ratings themselves are disclosed, thus achieving strictly-private collaborative filtering. The technique has been evaluated using the recently released Netflix prize dataset.
Enhancing privacy and preserving accuracy of a distributed collaborative filtering BIBAFull-Text 9-16
  Shlomo Berkovsky; Yaniv Eytani; Tsvi Kuflik; Francesco Ricci
Collaborative Filtering (CF) is a powerful technique for generating personalized predictions. CF systems are typically based on a central storage of user profiles used for generating the recommendations. However, such centralized storage introduces a severe privacy breach, since the profiles may be accessed for purposes, possibly malicious, not related to the recommendation process. Recent researches proposed to protect the privacy of CF by distributing the profiles between multiple repositories and exchange only a subset of the profile data, which is useful for the recommendation. This work investigates how a decentralized distributed storage of user profiles combined with data modification techniques may mitigate some privacy issues. Results of experimental evaluation show that parts of the user profiles can be modified without hampering the accuracy of CF predictions. The experiments also indicate which parts of the user profiles are most useful for generating accurate CF predictions, while their exposure still keeps the essential privacy of the users.
Trust-aware recommender systems BIBAFull-Text 17-24
  Paolo Massa; Paolo Avesani
Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.
The influence limiter: provably manipulation-resistant recommender systems BIBAFull-Text 25-32
  Paul Resnick; Rahul Sami
An attacker can draw attention to items that don't deserve that attention by manipulating recommender systems. We describe an influence-limiting algorithm that can turn existing recommender systems into manipulation-resistant systems. Honest reporting is the optimal strategy for raters who wish to maximize their influence. If an attacker can create only a bounded number of shills, the attacker can mislead only a small amount. However, the system eventually makes full use of information from honest, informative raters. We describe both the influence limits and the information loss incurred due to those limits in terms of information-theoretic concepts of loss functions and entropies.

Algorithms: collaborative filtering

Distributed collaborative filtering with domain specialization BIBAFull-Text 33-40
  Shlomo Berkovsky; Tsvi Kuflik; Francesco Ricci
User data scarcity has always been indicated among the major problems of collaborative filtering recommender systems. That is, if two users do not share sufficiently large set of items for whom their ratings are known, then the user-to-user similarity computation is not reliable and a rating prediction for one user can not be based on the ratings of the other. This paper shows that this problem can be solved, and that the accuracy of collaborative recommendations can be improved by: a) partitioning the collaborative user data into specialized and distributed repositories, and b) aggregating information coming from these repositories. This paper explores a content-dependent partitioning of collaborative movie ratings, where the ratings are partitioned according to the genre of the movie and presents an evaluation of four aggregation approaches. The evaluation demonstrates that the aggregation improves the accuracy of a centralized system containing the same ratings and proves the feasibility and advantages of a distributed collaborative filtering scenario.
Complex-network theoretic clustering for identifying groups of similar listeners in p2p systems BIBAFull-Text 41-48
  Amelie Anglade; Marco Tiemann; Fabio Vignoli
This article presents an approach to automatically create virtual communities of users with similar music preferences in a distributed system. Our goal is to create personalized music channels for these communities using the content shared by its members in peer-to-peer networks for each community. To extract these communities a complex network theoretic approach is chosen. A fully connected graph of users is created using epidemic protocols. We show that the created graph sufficiently converges to a graph created with a centralized algorithm after a small number of protocol iterations. To find suitable techniques for creating user communities, we analyze graphs created from real-world recommender datasets and identify specific properties of these datasets. Based on these properties, different graph-based community-extraction techniques are chosen and evaluated. We select a technique that exploits identified properties to create clusters of music listeners. The suitability of this technique is validated using a music dataset and two large movie datasets. On a graph of 6,040 peers, the selected technique assigns at least 85% of the peers to optimal communities, and obtains a mean classification error of less than 0.05% over the remaining peers that are not assigned to the best community.
Robust collaborative filtering BIBAFull-Text 49-56
  Bhaskar Mehta; Thomas Hofmann; Wolfgang Nejdl
The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their benefit.
   Robust statistics is an area within statistics where estimation methods have been developed that deteriorate more gracefully in the presence of unmodeled noise and slight departures from modeling assumptions. In this work, we study how such robust statistical methods, in particular M-estimators, can be used to generate stable recommendation even in the presence of noise and spam. To that extent, we present a Robust Matrix Factorization algorithm and study its stability. We conclude that M-estimators do not add significant stability to recommendation; however the presented algorithm can outperform existing recommendation algorithms in its recommendation quality.
A recursive prediction algorithm for collaborative filtering recommender systems BIBAFull-Text 57-64
  Jiyong Zhang; Pearl Pu
Collaborative filtering (CF) is a successful approach for building online recommender systems. The fundamental process of the CF approach is to predict how a user would like to rate a given item based on the ratings of some nearest-neighbor users (user-based CF) or nearest-neighbor items (item-based CF). In the user-based CF approach, for example, the conventional prediction procedure is to find some nearest-neighbor users of the active user who have rated the given item, and then aggregate their rating information to predict the rating for the given item. In reality, due to the data sparseness, we have observed that a large proportion of users are filtered out because they don't rate the given item, even though they are very close to the active user. In this paper we present a recursive prediction algorithm, which allows those nearest-neighbor users to join the prediction process even if they have not rated the given item. In our approach, if a required rating value is not provided explicitly by the user, we predict it recursively and then integrate it into the prediction process. We study various strategies of selecting nearest-neighbor users for this recursive process. Our experiments show that the recursive prediction algorithm is a promising technique for improving the prediction accuracy for collaborative filtering recommender systems.

User issues in recommender systems

Supporting product selection with query editing recommendations BIBAFull-Text 65-72
  Derek Bridge; Francesco Ricci
Consider a conversational product recommender system in which a user repeatedly edits and resubmits a query until she finds a product that she wants. We show how an advisor can: observe the user's actions; infer constraints on the user's utility function and add them to a user model; use the constraints to deduce which queries the user is likely to try next; and advise the user to avoid those that are unsatisfiable. We call this information recommendation. We give a detailed formulation of information recommendation for the case of products that are described by a set of Boolean features. Our experimental results show that if the user is given advice, the number of queries she needs to try before finding the product of highest utility is greatly reduced. We also show that an advisor that confines its advice to queries that the user model predicts are likely to be tried next will give shorter advice than one whose advice is unconstrained by the user model.
Incorporating user control into recommender systems based on naive Bayesian classification BIBAFull-Text 73-80
  Verus Pronk; Wim Verhaegh; Adolf Proidl; Marco Tiemann
Recommender systems are increasingly being employed to personalize services, such as on the web, but also in electronics devices, such as personal video recorders. These recommenders learn a user profile, based on rating feedback from the user on, e.g., books, songs, or TV programs, and use machine learning techniques to infer the ratings of new items.
   The techniques commonly used are collaborative filtering and naive Bayesian classification, and they are known to have several problems, in particular the cold-start problem and its slow adaptivity to changing user preferences. These problems can be mitigated by allowing the user to set up or manipulate his profile.
   In this paper, we propose an extension to the naive Bayesian classifier that enhances user control. We do this by maintaining and flexibly integrating two profiles for a user, one learned by rating feedback, and one created by the user. We in particular show how the cold-start problem is mitigated.
Replaying live-user interactions in the off-line evaluation of critique-based mobile recommendations BIBAFull-Text 81-88
  Quang Nhat Nguyen; Francesco Ricci
Supporting conversational approaches in mobile recommender systems is challenging because of the inherent limitations of mobile devices and the dependence of produced recommendations on the context. In a previous work, we proposed a critique-based mobile recommendation approach and presented the results of a live users evaluation. Live-user evaluations are expensive and there we could not compare different system variants to check all our research hypotheses. In this paper, we present an innovative simulation methodology and its use in the comparison of different user-query representation approaches. Our simulation test procedure replays off-line, against different system variants, interactions recorded in the live-user evaluation. The results of the simulation tests show that the composite query representation, which employs both logical and similarity queries, does improve the recommendation performance over a representation using either a logical or a similarity query.
Conversational recommenders with adaptive suggestions BIBAFull-Text 89-96
  Paolo Viappiani; Pearl Pu; Boi Faltings
We consider a conversational recommender system based on example-critiquing where some recommendations are suggestions aimed at stimulating preference expression to acquire an accurate preference model. User studies show that suggestions are particularly effective when they present additional opportunities to the user according to the look-ahead principle [32].
   This paper proposes a strategy for producing suggestions that exploits prior knowledge of preference distributions and can adapt relative to users' reactions to the displayed examples.
   We evaluate the approach with simulations using data acquired by previous interactions with real users. In two different settings, we measured the effects of prior knowledge and adaptation strategies with satisfactory results.

Algorithms: learning

Addressing uncertainty in implicit preferences BIBAFull-Text 97-104
  Sandra Clara Gadanho; Nicolas Lhuillier
The increasing amount of content available via digital television has made TV program recommenders valuable tools. In order to provide personalized recommendations, recommender systems need to collect information about user preferences. Since users are reluctant to invest much time in explicitly expressing their interests, preferences often need to be implicitly inferred through data gathered by monitoring user behavior. Which is, alas, less reliable.
   This article addresses the problem of learning TV preferences based on tracking the programs users have watched, whilst dealing with the varying degrees of reliability in such information. Three approaches to the problem are discussed: use all information equally; weight information by its reliability or simply discard the most unreliable information.
   Experimental results for these three approaches are presented and compared using a content-based filtering recommender built on a Naïve Bayes classifier.
Robustness of collaborative recommendation based on association rule mining BIBAFull-Text 105-112
  J. J. Sandvig; Bamshad Mobasher; Robin Burke
Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some model-based techniques are more robust than k-nn. Model abstraction can inhibit certain aspects of an attack, providing an algorithmic approach to minimizing attack effectiveness. In this paper, we examine the robustness of a recommendation algorithm based on the data mining technique of association rule mining. Our results show that the Apriori algorithm offers large improvement in stability and robustness compared to k-nearest neighbor and other model-based techniques we have studied. Furthermore, our results show that Apriori can achieve comparable recommendation accuracy to k-nn.
Usage-based web recommendations: a reinforcement learning approach BIBAFull-Text 113-120
  Nima Taghipour; Ahmad Kardan; Saeed Shiry Ghidary
Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Users are very often overwhelmed by the huge amount of information and are faced with a big challenge to find the most relevant information in the right time. Recommender systems aim at pruning this information space and directing users toward the items that best meet their needs and interests. Web Recommendation has been an active application area in Web Mining and Machine Learning research. In this paper we propose a novel machine learning perspective toward the problem, based on reinforcement learning. Unlike other recommender systems, our system does not use the static patterns discovered from web usage data, instead it learns to make recommendations as the actions it performs in each situation. We model the problem as Q-Learning while employing concepts and techniques commonly applied in the web usage mining domain. We propose that the reinforcement learning paradigm provides an appropriate model for the recommendation problem, as well as a framework in which the system constantly interacts with the user and learns from her behavior. Our experimental evaluations support our claims and demonstrate how this approach can improve the quality of web recommendations.
Improving new user recommendations with rule-based induction on cold user data BIBAFull-Text 121-128
  An-Te Nguyen; Nathalie Denos; Catherine Berrut
With recommender systems, users receive items recommended on the basis of their profile. New users experience the cold start problem: as their profile is very poor, the system performs very poorly. In this paper, classical new user cold start techniques are improved by exploiting the cold user data, i.e. the user data that is readily available (e.g. age, occupation, location, etc.), in order to automatically associate the new user with a better first profile. Relying on the existing α-community spaces model, a rule-based induction process is used and a recommendation process based on the "level of agreement" principle is defined. The experiments show that the quality of recommendations compares to that obtained after a classical new user technique, while the new user effort is smaller as no initial ratings are asked.

Research short papers

A probabilistic model for item-based recommender systems BIBAFull-Text 129-132
  Ming Li; Benjamin Dias; Wael El-Deredy; Paulo J. G. Lisboa
Recommender systems estimate the conditional probability P(χj|χi) of item χj being bought, given that a customer has already purchased item χi. While there are different ways of approximating this conditional probability, the expression is generally taken to refer to the frequency of co-occurrence of items in the same basket, or other user-specific item lists, rather than being seen as the co-occurrence of χj with χi as a proportion of all other items bought alongside χi. This paper proposes a probabilistic calculus for the calculation of conditionals based on item rather than basket counts. The proposed method has the consequence that items bough together as part of small baskets are more predictive of each other than if they co-occur in large baskets. Empirical results suggests that this may result in better take-up of personalised recommendations.
A recommender system for on-line course enrollment: an initial study BIBAFull-Text 133-136
  Michael P. O'Mahony; Barry Smyth
In this paper we report on our work to date concerning the development of a course recommender system for University College Dublin's on-line enrollment application. We outline the factors that influence student choices and propose solutions to address some of the key considerations that are identified. We empirically evaluate our approach using historical student enrollment data and show that promising performance is achieved with our initial design.
Case Amazon: ratings and reviews as part of recommendations BIBAFull-Text 137-140
  Juha Leino; Kari-Jouko Räihä
We studied user behavior in a recommender-rich environment, Amazon online store, to see what role the algorithm-based and user-generated recommendations play in finding items of interest. We used applied ethnography, on-location interviewing and observation, to get an accurate picture of user activity. We were especially interested in the role of customer ratings and reviews and what kind of strategies users had developed for such an environment. Our results underline the need to develop recommender systems as a whole. The way the recommendations are shown affects which items get picked, and for improving the interface, it is necessary to study the whole in addition to studying the parts in isolation.
Comparing and evaluating information retrieval algorithms for news recommendation BIBAFull-Text 141-144
  Toine Bogers; Antal van den Bosch
In this paper, we argue that the performance of content-based news recommender systems has been hampered by using relatively old and simple matching algorithms. Using more current probabilistic retrieval algorithms results in significant performance boosts. We test our ideas on a test collection that we have made publicly available. We perform both binary and graded evaluation of our algorithms and argue for the need for more graded evaluation of content-based recommender systems.
Influence-based collaborative active learning BIBAFull-Text 145-148
  Neil Rubens; Masashi Sugiyama
In order to learn a user's preferences in collaborative recommender systems it is crucial to select the most informative items for a user to rate. For example, rating a popular item will provide little discriminative information about user's preferences since most users like popular items. Existing approaches select the most informative items based primarily on items' uncertainty, but tend to ignore an important metric of coverage -- the number of items for which we are able to accurately estimate preferences. Selecting an item based only on uncertainty will reduce the uncertainty of the selected item, but will not necessarily reduce the uncertainty of other items -- which is the ultimate goal. Therefore, in order to reduce the uncertainty over all items, we propose to select items that are not only uncertain but are also influential. Experimental results demonstrate the advantages of the proposed approach.
Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering BIBAFull-Text 149-152
  Tavi Nathanson; Ephrat Bitton; Ken Goldberg
Recommender systems strive to recommend items that users will appreciate and rate highly, often presenting items in order of highest predicted ratings first. In this working paper we present Eigentaste 5.0, a constant-time recommender system that dynamically adapts the order that items are recommended by integrating user clustering with item clustering and monitoring item portfolio effects. This extends our Eigentaste 2.0 algorithm, which uses principal component analysis to cluster users offline. In preliminary experiments we backtested Eigentaste 5.0 on data collected from Jester, our online joke recommender system. Results suggest that it will perform better than Eigentaste 2.0. The new algorithm also uses item clusters to address the cold-start problem for introducing new items.
Effective explanations of recommendations: user-centered design BIBAFull-Text 153-156
  Nava Tintarev; Judith Masthoff
This paper characterizes general properties of useful, or Effective, explanations of recommendations. It describes a methodology based on focus groups, in which we elicit what helps moviegoers decide whether or not they would like a movie. Our results highlight the importance of personalizing explanations to the individual user, as well as considering the source of recommendations, user mood, the effects of group viewing, and the effect of explanations on user expectations.
Evaluating information presentation strategies for spoken recommendations BIBAFull-Text 157-160
  Andi Winterboer; Johanna D. Moore
We report the results of a Wizard-of-Oz (WoZ) study comparing two approaches to presenting information in a spoken dialogue system generating flight recommendations. We found that recommendations presented using the user-model based summarize and refine (UMSR) approach enable more efficient information retrieval than the data-driven summarize and refine (SR) approach. In addition, user ratings on four evaluation criteria showed a clear preference for recommendations based on the UMSR approach.
Leveraging aggregate ratings for better recommendations BIBAFull-Text 161-164
  Akhmed Umyarov; Alexander Tuzhilin
The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions.
Supporting social recommendations with activity-balanced clustering BIBAFull-Text 165-168
  F. Maxwell Harper; Shilad Sen; Dan Frankowski
In support of social interaction and information sharing, online communities commonly provide interfaces for users to form or interact with groups. For example, a user of the social music recommendation site last.fm might join the "First Wave Punk" group to discuss his or her favorite band (The Clash) and listen to playlists generated by fellow fans. Clustering techniques provide the potential to automatically discover groups of users who appear to share interests. We explore this idea by describing algorithms for clustering users of an online community and automatically describing the resulting user groups. We designed these techniques for use in an online recommendation system with no pre-existing group functionality, which led us to develop an "activity-balanced clustering" algorithm that considers both user activity and user interests in forming clusters.
The evaluation of a hybrid critiquing system with preference-based recommendations organization BIBAFull-Text 169-172
  Li Chen; Pearl Pu
The critiquing-based recommender system mainly aims to guide users to make an accurate and confident decision, while requiring them to consume a low level of effort. We have previously found that the hybrid critiquing system of combining the strengths from both system-proposed critiques and user self-motivated critiquing facility can highly improve users' subjective perceptions such as their decision confidence and trusting intentions. In this paper, we continue to investigate how to further reduce users' objective decision effort (e.g. time consumption) in such system by increasing the critique prediction accuracy of the system-proposed critiques. By means of real user evaluation, we proved that a new hybrid critiquing system design that integrates the preference-based recommendations organization technique for critiques suggestion can effectively help to increase the proposed critiques' application frequency and significantly contribute to saving users' task time and interaction effort.
The KeepUP recommender system BIBAFull-Text 173-176
  Andrew Webster; Julita Vassileva
In this short paper, we describe our RSS recommender system, KeepUP. Too often recommender systems are seen as black box systems, resulting in general perplexity and dissatisfaction from users who are treated as passive, isolated consumers. Recent literature observes that recommendations rarely occur within such isolation and that there may be potential within more socially-orientated approaches. With KeepUP, we outline the design of a recommendation process that is based on an implicit social network where the relevancy and meaning of information can be negotiated not only with the recommender system but also with other users. Our overall goal is to support the formation and development of online communities of interest.
Towards ensemble learning for hybrid music recommendation BIBAFull-Text 177-178
  Marco Tiemann; Steffen Pauws
We investigate ensemble learning methods for hybrid music recommender algorithms, combining a social and a content-based recommender algorithm as weak learners by applying a combination rule to unify the weak learners' output. A first experiment suggests that such a combination can already reduce the mean absolute prediction error compared to the weak learners' individual errors.
Toward the exploitation of social access patterns for recommendation BIBAFull-Text 179-182
  Jill Freyne; Rosta Farzan; Maurice Coyle
The size and diversity of the Web has been the root cause of the poor performance of many retrieval systems, with little navigational support provided by many large online formation repositories. The online information retrieval process cross different repositories shares similarities with content access facilities and user behaviors even when containing inherently different content types. In this work, we introduce our social recommender system called ASSIST. The recommendation framework in ASSIST can be applied to any online information retrieval service with key information access components, search and browsing. ASSIST exploits multiple forms of social implicit feedback in order to generate well-informed user recommendations in the online information retrieval domain.

Practice/industry track abstracts

TechLens: a researcher's desktop BIBAFull-Text 183-184
  Nishikant Kapoor; Jilin Chen; John T. Butler; Gary C. Fouty; James A. Stemper; John Riedl; Joseph A. Konstan
Rapid and continuous growth of digital libraries, coupled with brisk advancements in technology, has driven users to seek tools and services that are not only customized to their specific needs, but are also helpful in keeping them stay abreast with the latest developments in their field. TechLens is a recommender system that learns about its users through implicit feedback, builds correlations among them, and uses that information to generate recommendations that match the user's profile. It gives users control over which parts of their profile of known citations are used in forming recommendations for new articles. This demonstration is a prototype that showcases some of the tools and services that TechLens offers to the users of digital libraries.
The challenges of recommending digital selves in physical spaces BIBAFull-Text 185-186
  Joseph F. McCarthy
There are a number of online systems where people can, in effect, recommend themselves, or more precisely, represent themselves in ways that may motivate others to seek them out for conversations, business ventures, dates and a variety of other types of interactions and relationships. Several of these systems offer capabilities for people to create profiles of themselves and algorithms for matching profiles to recommend one person to another. When a group of people is gathered together in a physical space, for the purpose of renewing connections or creating new ones, new challenges emerge with respect to which dimensions of people's online representations to inject into that space -- how, where and when to recommend whom to whom. This paper briefly describes some experiences and ongoing challenges encountered in determining how best to bridge the gaps between people by bridging the gaps between people's online representations of themselves and their presence in physical space.

Doctoral symposium

A hybrid social-acoustic recommendation system for popular music BIBAFull-Text 187-190
  Justin Donaldson
Recommendation systems leverage several types of information relating to a recommendable item. The recommendation methods are often based on the analysis of how a set of users associate or rate a given set of items, but they can also focus on the analysis of how the content of the items is related. This paper discusses a hybrid recommendation system for music -- a system that leverages both spectral graph properties of an item-based collaborative filtering association network as well as acoustic features of the underlying music signal. Both features are balanced appropriately and used to disambiguate the music-seeking intentions of a user.
Evaluating sources of implicit feedback in web searches BIBAFull-Text 191-194
  Xin Fu
The study investigates the relationship between the types of behavior that can be captured from Web searches and searchers' interests. Web search cases which involve underspecification of information needs at the beginning and modification of search strategies during the search process will be collected and examined by human analysts. The study focuses on identifying the rules used by analysts to infer searcher interests. These rules can be put into algorithms as the basis for systems that provide query modification suggestions or implicitly reformulate the query as the searcher continues to work.
A multiagent knowledge-based recommender approach with truth maintenance BIBAFull-Text 195-198
  Fabiana Lorenzi
This thesis investigates a way of using knowledge in dynamic and distributed domains for supporting recommendation, keeping the consistence of the decision knowledge that change over time. We propose the use of a multiagent knowledge-based recommender approach capable of dealing with distributed expert knowledge in order to support travel agents in recommending tourism packages. Agents work as experts cooperating and communicating to each other in the recommendation process. Each agent has a truth maintenance system (TMS) component that helps the agents to keep the integrity of their knowledge bases.
Elicitation of profile attributes by transparent communication BIBAFull-Text 199-202
  Mike Radmacher
When people are seeking information they are interested in, they need time, need to know exactly what they are looking for and require attention capacities to check different sources. Recommender systems help to overcome the information overflow and filter out irrelevant sources by comparing different types of information and selecting the best results in consideration of customer preferences. Therefore accurate customer profiles are necessary which nowadays do not exist. In a mobile environment customers are not willing to spend time on disclosing their preferences; maybe they are not aware of them or have difficulties to respond to system requests. The paper in hand follows recommendations by critiquing to improve profiling quality but instead of collecting information, the transparent communication of profile extensions is focused. The customer can add preferences to his profile without explicitly expressing. Furthermore, the connection between proposed preferences and the systems conclusion behind is visible.
Explanations of recommendations BIBAFull-Text 203-206
  Nava Tintarev
This thesis focuses on explanations of recommendations. Explanations can have many advantages, from inspiring user trust to helping users make good decisions. We have identified seven different aims of explanations, and in this thesis we will consider how explanations can be optimized for some of these aims. We will consider both an explanation's content and its presentation. As a domain, we are currently investigating explanations for a movie recommender, and developing a prototype system. This paper summarizes the goals of the thesis, the methodology we are using, the work done so far and our intended future work.
Can social information retrieval enhance the discovery and reuse of digital educational content? BIBAFull-Text 207-210
  Riina Vuorikari
This paper gives an extended abstract of the dissertation work seeking to find how social information retrieval can enhance the discovery and reuse of digital educational content. A social bookmarking and tagging tool, that is used in a multi-lingual and multi-cultural context of Europe, is introduced to teachers. We intend to use this information to create social information retrieval mechanisms that allow flexible access to large-scale collections of digital educational content.
   A first step towards studying the design and implementation of such systems is to understand more about tagging in multiple languages, its underlying data structures, and how multi-lingual tags and annotations can be leveraged for social information retrieval, such as for a recommender and a social navigation system. Thereafter, we plan to study their acceptance, use and usefulness.
   Moreover, our goal is to make the discovery of digital educational content more useful and efficient for teachers by studying the relationship between different information seeking tasks and retrieval methods. We believe that this can facilitate, support and enhance the everyday tasks of teachers and learners when interacting with digital content for education.