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Enhancement of the Neutrality in Recommendation Enhancement of the Neutrality in Recommendation Toshihiro Kamishima, Shotaro Akaho, Jun Sakuma and Hideki Asoh University of Tsukuba National Institute of Advanced Industrial Science 1-1-1 Tennodai, Tsukuba, 305-8577 Japan; and and Technology (AIST) Japan Science and Technology Agency AIST Tsukuba Central 2, Umezono 1-1-1, 4-1-8, Honcho, Kawaguchi, Saitama, 332-0012 Tsukuba, Ibaraki, 305-8568 Japan Japan [email protected], [email protected] [email protected], [email protected] ABSTRACT The influence of personalization technologies such as recom- This paper proposes an algorithm for making recommenda- mender systems or personalized search engines on people's tion so that the neutrality toward the viewpoint specified decision making is getting stronger and stronger. For exam- by a user is enhanced. This algorithm is useful for avoid- ple, at a shopping site, if a customer checks a recommen- ing to make decisions based on biased information. Such dation list and finds a five-star-rated item, he/she would a problem is pointed out as the filter bubble, which is the more seriously consider buying the highly rated item. The influence in social decisions biased by a personalization tech- filter bubble, which is the selection of the appropriate di- nology. To provide such a recommendation, we assume that versity of information provided to users, is one of problems a user specifies a viewpoint toward which the user want accompanying the growing influence of recommendation or to enforce the neutrality, because recommendation that is personalization. neutral from any information is no longer recommendation. Given such a target viewpoint, we implemented information The problem of the filter bubble was recently posed by Pariser neutral recommendation algorithm by introducing a penalty [17]. Via the influence of personalized technologies, the top- term to enforce the statistical independence between the tar- ics of information provided to users are becoming restricted get viewpoint and a preference score. We empirically show to those originally preferred by them, and this restriction is that our algorithm enhances the independence toward the not notified by users. This situation is compared to shut- specified viewpoint by and then demonstrate how sets of ting up each individual in a separate bubble. Pariser claimed recommended items are changed. that due to the obstruction of these bubbles, users lose the opportunity of finding new topics and that sharing public Categories and Subject Descriptors matters throughout our society is getting harder. To dis- H.3.3 [INFORMATION SEARCH AND RETRIEVAL]: cuss this filter bubble problem, a panel discussion was held Information filtering at the RecSys 2011 conference [20]. During the RecSys panel discussion, panelists made the fol- Keywords lowing assertions about the filter bubble problem. Biased neutrality, fairness, filter bubble, collaborative filtering, ma- topics would be certainly selected by the influence of per- trix decomposition, information theory sonalization, but at the same time, it would be intrinsically impossible to make recommendations that are absolutely 1. INTRODUCTION neutral from any viewpoint, and the diversity of provided A recommender system searches for items and information topics intrinsically has a trade-off relation to the fitness of that would be useful to a user based on the user's behaviors these topics for users' interests or needs. To recommend or the features of candidate items [21, 2]. GroupLens [19] something, or more generally to select something, one must and many other recommender systems emerged in the mid- consider the specific aspect of a thing and must ignore the 1990s, and further experimental and practical systems have other aspects of the thing. The panelists also pointed out been developed during the explosion of Internet merchan- that current recommender systems fail to satisfy users' in- dizing. In the past decade, such recommender systems have formation need that they search for a wide variety of topics been introduced and managed at many e-commerce sites to in the long term. promote items sold at these sites. To solve this problem, we propose an information neutral recommender system that guarantees the neutrality of rec- ommendation results. As pointed out during the RecSys 2011 panel discussion, because it is impossible to make a Paper presented at the 2012 Decisions@RecSys workshop in conjunction recommendation that is absolutely neutral from all view- c with the 6th ACM conference on Recommender Systems. Copyright points, we consider neutrality from the viewpoint or infor- 2012 for the individual papers by the papers’ authors. Copying permitted for private and academic purposes. This volume is published and copy- mation specified by a user. For example, users can specify a righted by its editors. feature of an item, such as a brand, or a user feature, such 2012 Decisions@RecSys workshop September 9, 2012, Dublin, Ireland as a gender or an age, as a viewpoint. An information neu- 8 tral recommender system is designed so that these specified from Google2 for the query \Egypt" during the Egyptian features will not affect recommendation results. This sys- uprising in 2011 from various people. Even though such a tem can also be used to avoid the use of information that highly important event was occurring, only sightseeing pages is restricted by law or regulation. For example, the use of were listed for some users instead of news pages about the some information is prohibited for the purpose of making Egyptian uprising, due to the influence of personalization. recommendation by privacy policies. In this example, he claimed that personalization technology spoiled the opportunity to obtain information that should We borrowed the idea of fairness-aware mining, which we be commonly shared in our society. proposed earlier [11], to build this information neutral rec- ommender system. To enhance the neutrality or the inde- We consider that Pariser's claims can be summarized as fol- pendence in recommendation, we introduce a regularization lows. The first point is the problem that users lost opportu- term that represents the mutual information between a rec- nities to obtain information about a wide variety of topics. ommendation result and the specified viewpoint. A chance to know things that could make users' lives fruit- ful was lessened. The second point is the problem that each Our contributions are as follows. First, we present a defini- individual obtains information that is too personalized, and tion of neutrality in recommendation based on the consider- thus the amount of shared information is decreased. Pariser ation of why it is impossible to achieve an absolutely neutral claimed that the loss of sharing information is a serious ob- recommendation. Second, we propose a method to enhance stacle for building consensus in our society. He claimed that the neutrality that we defined and combine it with a latent the loss of the ability to share information is a serious ob- factor recommendation model. Finally, we demonstrate that stacle for building consensus in our society. the neutrality of recommendation can be enhanced and how recommendation results change by enhancing the neutrality. RecSys 2011, which is a conference on recommender systems, held a panel discussion the topic of which was this filter bub- In section 2, we discuss the filter bubble problem and neu- ble problem [20]. This panel concentrated on the following trality in recommendation and define the goal of an infor- three arguing points. (1) Are there filter bubbles? Resnick mation neutral recommender task. An information neutral pointed out the possibility that personalization technologies recommender system is proposed in section 3, and its ex- narrow users' experience in the mid 1990s. Because select- perimental results are shown in section 4. Sections 5 and 6 ing specific information by definition leads to ignoring other cover related work and our conclusion, respectively. information, the diversity of users' experiences intrinsically have a trade-off relation to the fitness of information for users' interests. As seen in the difference between the per- 2. INFORMATION NEUTRALITY spective of al-Jazeera and that of Fox News, this problem In this section, we discuss information neutrality in recom- exists irrespective of personalization. Further, given signals mendation based on the considerations on the filter bubble or expressions of users' interest, it is difficult to adjust how problem and the ugly duckling theorem. much a system should meet those interests. 2.1 The Filter Bubble Problem (2) To what degree is personalized filtering a problem? There is no absolutely neutral viewpoint. On the other hand, the We here summarize the filter bubble problem posed by Pariser use of personalized filtering is inevitable, because it is not and the discussion in the panel about this problem at the feasible to exhaustively access the vast amount of informa- RecSys 2011 conference. The filter bubble problem is a tion in the universe. One potential concern is the effect of concern that personalization technologies, including recom- selective exposure, which is the tendency to get reinforce- mender systems, narrow and bias the topics of information ment of what people already believe. According to the re- provided to people while they don't notice these facts [17]. sults of studies about this concern, this is not so serious, because people viewing extreme sites spend more time on Pariser demonstrated the following examples in a TED talk mainstream news as well. about this problem [16]. In a social network service, Face- book1, users have to specify a group of friends with whom (3) What should we as a community do to address the filter they can chat or have private discussions. To help users bubble issue? To adjust the trade-off between diversity and find their friends, the service has a function to list other fitness of information, a system should consider users' im- users' accounts that are expected to be related to a user.
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