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Trattner Christoph, Parra Denis, Brusilovsky Peter, Marinho Leandro

Report on the SIGIR 2015 Workshop on Social Personalization and Search


The use of contexts –side information associated to information tasks– has been one ofthe most important dimensions for the improvement of Information Retrieval tasks, helpingto clarify the information needs of the users which usually start from a few keywords in atext box. Particularly, the social context has been leveraged in search and personalizationsince the inception of the Social Web, but even today we find new scenarios of informationfiltering, search, recommendation and personalization where the use of social signals canproduce a steep improvement. In addition, the action of searching has become a social processon the Web, making traditional assumptions of relevance obsolete and requiring newparadigms for matching the most useful resources that solve information needs. This escenariohas motivated us for organizing the Social Personalization and Search (SPS) workshop,a forum aimed at sharing and discussing research that leverage social data for improvingclassic personalization models for information access and to revisiting search from individualphenomena to a collaborative process.

Trattner Christoph, Balby Marinho Leandro, Parra Denis

Are Real-World Place Recommender Algorithms Useful in Virtual World Environments?

Proceedings of the 9th {ACM} Conference on Recommender Systems, ACM, 2015

Large scale virtual worlds such as massive multiplayer online gamesor 3D worlds gained tremendous popularity over the past few years.With the large and ever increasing amount of content available, virtualworld users face the information overload problem. To tacklethis issue, game-designers usually deploy recommendation serviceswith the aim of making the virtual world a more joyful environmentto be connected at. In this context, we present in this paper the resultsof a project that aims at understanding the mobility patternsof virtual world users in order to derive place recommenders forhelping them to explore content more efficiently. Our study focuson the virtual world SecondLife, one of the largest and mostprominent in recent years. Since SecondLife is comparable to realworldLocation-based Social Networks (LBSNs), i.e., users canboth check-in and share visited virtual places, a natural approach isto assume that place recommenders that are known to work well onreal-world LBSNs will also work well on SecondLife. We have putthis assumption to the test and found out that (i) while collaborativefiltering algorithms have compatible performances in both environments,(ii) existing place recommenders based on geographicmetadata are not useful in SecondLife.

Trattner Christoph, Parra Denis , Brusilovsky Peter, , Marinho Leandro

SPS'15: 2015 International Workshop on Social Personalization & Search

Proceedings of the 38th International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval, ACM, 2015


Lacic Emanuel, Kowald Dominik, Eberhard Lukas, Trattner Christoph, Parra Denis, Marinho Leandro

Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces

Mining, Modeling, and Recommending'Things' in Social Media, MSM'2015, Springer, 2015

Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. To contribute to this sparse field of research, in this paper we exploit users’ interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (CF) approach and in combination via a hybrid recommender algorithm, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.
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