Session: Applications

Chair: Alexander Felfernig
Date: Monday, September 27, 15:50-17:30

• Search shortcuts: a new approach to the recommendation of queries

  by Ranieri Baraglia, Fidel Cacheda, Victor Carneiro, Diego Fernandez, Vreixo Formoso, Raffaele Perego, Fabrizio Silvestri

  The recommendation of queries, known as query suggestion, is a common practice on major Web Search Engines. It aims to help users to find the information they are looking for, and is usually based on the knowledge learned from past interactions with the search engine. In this paper we propose a new model for query suggestion, the Search Shortcut Problem, that consists in recommending “successful” queries that allowed other users to satisfy, in the past, similar information needs. This new model has several advantages with respect to traditional query suggestion approaches. First, it allows a straightforward evaluation of algorithms from available query log data. Moreover, it simplifies the application of several recommendation techniques from other domains. Particularly, in this work we applied Collaborative Filtering to this problem, and evaluated the interesting results achieved on large query logs from AOL and Microsoft. Different techniques for analyzing and extracting information from query logs, as well as new metrics and techniques for measuring the effectiveness of recommendations are proposed and evaluated. The results obtained clearly show the importance of several of our contributions, and open an interesting field for future research.

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• Increasing engagement through early recommender intervention

  by Jill Freyne, Michal Jacovi, Ido Guy, Werner Geyer

  Social network sites rely on the contributions of their members to create a lively and enjoyable space. Recent research has focused on using personalization and recommender technologies to encourage participation of existing members. In this work we present an early-intervention approach to encouraging participation and engagement, which makes recommendations to new users during their sign-up process. Our recommender system exploits external social media to produce people and profile entry recommendations for new users. We present results of a live user study, showing that users who received recommendations at sign-up created more social connections, contributed more content, and were on the whole more engaged with the system, contributing more without prompt and returning more often. We further show that recommendations for multiple content types yield significantly better results, in terms of user contribution and consumption; and that recommendations of more active users yield a higher return rate.

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• Recommending new movies: even a few ratings are more valuable than metadata

  by István Pilászy, Domonkos Tikk

  The Netflix Prize (NP) competition gave much attention to collaborative filtering (CF) approaches. Matrix factorization (MF) based CF approaches assign low dimensional feature vectors to users and items. We link CF and content-based filtering (CBF) by finding a linear transformation that transforms user or item descriptions so that they are as close as possible to the feature vectors generated by MF for CF. We propose methods for explicit feedback that are able to handle 140,000 features when feature vectors are very sparse. With movie metadata collected for the NP movies we show that the prediction performance of the methods is comparable to that of CF, and can be used to predict user preferences on new movies. We also investigate the value of movie metadata compared to movie ratings in regards of predictive power. We compare our solely CBF approach with a simple baseline rating-based predictor. We show that even 10 ratings of a new movie are more valuable than its metadata for predicting user ratings.

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• Regret-based optimal recommendation sets in conversational recommender systems

  by Paolo Viappiani, Craig Boutilier

  Current conversational recommender systems are unable to offer guarantees on the quality of their recommendations due to a lack of principled user utility models. We develop an approach to recommender systems that incorporates an explicit utility model into the recommendation process in a decision-theoretically sound fashion. The system maintains explicit constraints on user utility based on preferences revealed by the user’s actions. We investigate a new decision criterion, setwise minimax regret (SMR), for constructing optimal recommendation sets: we develop algorithms for computing SMR, and prove that SMR determines choice sets for queries that are myopically optimal. This provides a natural basis for generating compound critiques in conversational recommender systems. Our simulation results suggest that this utility-theoretically sound approach to user modeling allows much more effective navigation of a product space than traditional approaches based on, for example, heuristic utility models and product similarity measures.

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