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Preference Aggregation in Group and Social Recommender Systems George POPESCU HCI, I&C, EPFL

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Preference Aggregation in Group and Social Recommender Systems George POPESCU HCI, I&C, EPFL EDIC RESEARCH PROPOSAL 1 Preference Aggregation in Group and Social Recommender Systems George POPESCU HCI, I&C, EPFL Abstract—Aggregating preference and recommending a social elements (e.g. people, events or groups) that are likely to common set of items for a group has become a challenging be of interest to one or more users. They represent an emerging topic in recommender systems and social web sites. This issue technology which connects prediction theories and information is mainly concerned with eliciting individual users’ preferences, suggesting the maximized overall satisfaction outcome for all retrieval algorithms with social choice. Nowadays, RSs have users and ensuring that the aggregation mechanism is resistant to a ubiquitous nature in websites. They are crucial because they manipulation. In the following we survey preference aggregation help users make effective decisions, participate in information mechanisms for groups. First, we highlight the main challenges in filtering and allow companies to increase their revenue through group recommender systems. Then, we outline the main results product promotion and e-commerce. of the PolyLens study on recommending movies for groups. Thirdly, we describe methods for modeling individual and group In individual recommender systems the more effort users satisfaction which are essential for enhancing recommendation put in stating their preferences the more accurate recommen- quality. Finally, we briefly present our work on developing the dations theyr obtain. The recommendation algorithm compares GroupFun music recommender system together with a novel the collected information with similar and non-similar data preference aggregation algorithm. from other users and calculates a list of recommended items Index Terms—Algorithms, social choice, aggregation, human for the current one. Main challenges focus primarily on data factors, recommendation, satisfaction, utility, voting sparsity: RSs need a lot of information to effectively make recommendations. Furthermore, they are “biased towards the I. INTRODUCTION old and have difficulty showing new”: the issue of changing ECOMMENDER systems (RSs) attempt to recommend data. Also, users’ preferences change over time. This change R information items (movies, TV programs, videos, music, cannot be very precisely measured nor predicted. books, news, images, web pages, scientific literature, etc.) or Group recommender systems (GRSs) use various strategies to aggregate users’ preferences - which can be either implicit Proposal submitted to committee: August 23rd, 2011; Can- or explicit - into a common social welfare function which didacy exam date: August 30th, 2011; Candidacy exam would maximize the satisfaction of all members. On the one committee: Prof. Pierre Dillenbourg, Prof. Jeffrey Huang, Dr. hand, examples of implicit data collection include: observing Pearl Pu. Exam president, thesis director, co-examiner. the items that a user views in an online store, analyzing This research plan has been approved: item/user viewing times, keeping a record of the items that a user purchases online and obtaining a list of items that a user has listened to or watched on his/her computer. On the Date: ———————————— other, explicit data collection refers to: asking a user to rate an item on a given scale, rank a collection of items from favorite to least favorite, or create a list of items that he/she likes. Doctoral candidate: ———————————— The social welfare is an aggregate of individual utilities of all (G. Popescu) (name and signature) group members. For online applications voting is one of the most common ways for users to manifest their preferences. Algorithms input these votes to propose a list of items which Thesis director: ———————————— aim at increasing the group’s welfare. (P. Pu) (name and signature) The main goal of social choice theory is to answer “which strategy is most effective and will be most liked by a group of Thesis co-director: ———————————— users?”. With the purpose of determining what strategy people (if applicable) (name and signature) actually use experiments proved that individuals use computa- tionally simple strategies, particularly the average strategy, the average without misery and the least misery strategy. However, Doct. prog. director:———————————— there exists no dominant strategy as people switch between (R. Urbanke) (signature) them given a different context. Fairness plays an important role in decision making but usually group members do not 30.08.2011 have a clear strategy for applying it and trust algorithms. EDIC RESEARCH PROPOSAL 2 TABLE I GROUP DECISION RULES includes experimental results based on formulas designed to model and predict the satisfaction experienced by individuals Rule Summary Ind. Group using the Interactive TV group recommender system which Average Compute each alternative’s mean and High High recommends sequences of news items. winner choose the one with highest mean Median Compute each alternative’s median and High High II. MAIN CHALLENGES IN GROUP RECOMMENDER winner choose the one with highest median SYSTEMS Weighted Assign a weighted average value to High High average each alternative and choose the one In GRSs challenges are a lot more complex than in indivi- winner with the highest weighted average value dual recommender systems: users do not need to interact with Best The member who has achieved the High Med. member highest individual accuracy in estima- the system more and still obtain group satisfying recommen- ting alternative values is selected, and dations. Their preferences need to be understood by the this member’s first choices become the recommender following social rules. Also, users need to have group’s choices an incentive for stating their preferences truthfully for the Group Alternatives are considered one at a Med. Med. satisfying time in a random order. The first al- benefit of the entire group. The development of theoretical ternative for which all members’ value frameworks and applications is a research priority for the estimates exceed aspiration thresholds social and group recommender systems community. Findings is chosen by the group in this field are related to truthful preference elicitation, Plurality Each member assigns one vote to the Low Low alternative with the highest estimated recommendation understanding and user adoption. value. The one receiving the most votes is chosen A. Survey of group recommender systems Approval Vote for every acceptable alternative Low Low voting and the one with the most votes wins For one system to best reply to a group of users’ needs it Random One member is selected at random and Low Low has to be modeled as measuring both individual and group member his/her first choices are the group’s targets. In practice it is difficult to model groups simply by choices aggregating individual models. Various group recommenders implemented in the past two decades focused on recom- mending entertainment or interesting items to users to at- In Table 1 we summarize a list of group decision rules tract participation, e.g. CATS, Intrigue and Travel Decision that are commonly used in group recommender systems. Forum (travel destinations), PocketRestaurantFinder (restau- Individual cognitive effort indicates the level of effort an rants), MusicFX and Flytrap (music), PolyLens (movies), I- individual expends in participating in group decision. Social Spy (meta-search), AdaptiveRadio (radio stations), In-vehicle or group effort indicates the level of effort a group expends multimedia recommender (multimedia), TV program recom- while making a collective decision. Selecting the methods mender and TV4M (TV programs), Let’s browse (web pages), which require low effort and are computationally simple is etc. They all use rather simple voting mechanisms which “sum highly important for recommendation understanding. Other up” user preferences trying to maximize group satisfaction criteria for consideration include the method’s resistance to and minimize the deviation of each individual utility from the manipulation and its fairness for all group members. group utility. When designing a group and social recommender system The dynamics and usage statistics of these recommender several key characteristics should be considered. First of all, systems show large differences between small and large the system should allow group members to express and chance groups. In-depth analysis present decision aggregation me- their preferences by interacting with other users. Individuals thods and group satisfaction issues and tackle aspects related should be able to discover other members’ preferences leading to social interaction between group members. Many of the to increasing group awareness. The solution is usually based considered challenges demand more sophisticated algorithms on two approaches: an algorithm which takes into account which would increase the welfare or satisfaction for the entire everyone’s preferences and an interface which allows users group. Evaluating group satisfaction is yet another challenging to see other members’ interests. The former is a mapping research issue since both objective and subjective measure- of the social welfare function of the group while the latter ments are hard to quantify, for instance: fairness, decision corresponds to a negotiation model in which users trade their quality, group awareness, group interaction, etc. utility to arrive at a common output in a group setting. Sections II, III and IV present the three selected papers
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