ABSTRACT
An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on long-term user models that are pre-trained on historical data can therefore be insufficient for a suitable next-basket recommendation. Simple "real-time" recommendation approaches based, e.g., on unpersonalized co-occurrence patterns, on the other hand do not fully exploit the available information about the user's long-term preference profile. In this work, we aim to explore and quantify the effectiveness of using and combining long-term models and short-term adaptation strategies. We conducted an empirical evaluation based on a novel evaluation design and two real-world datasets. The results indicate that maintaining short-term content-based and recency-based profiles of the visitors can lead to significant accuracy increases. At the same time, the experiments show that the choice of the algorithm for learning the long-term preferences is particularly important at the beginning of new shopping sessions.
- G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. In Recommender Systems Handbook, pages 217--253. 2011. Google ScholarDigital Library
- S. R. Aghabozorgi and T. Y. Wah. Recommender systems: Incremental clustering on web log data. In Proc. ICIS '09, pages 812--818, 2009. Google ScholarDigital Library
- Y. AlMurtadha, N. B. Sulaiman, N. Mustapha, N. I. Udzir, and Z. Muda. ARS: Web page recommendation system for anonymous users based on web usage mining. In Proc. ECS '10, pages 115--120, 2010. Google ScholarDigital Library
- S. Anand and B. Mobasher. Contextual recommendation. In From Web to Social Web, volume 4737 of LNCS, pages 142--160. 2007. Google ScholarDigital Library
- L. Baltrunas and X. Amatriain. Towards time-dependant recommendation based on implicit feedback. In Proc. CARS WS at RecSys '09, 2009.Google Scholar
- L. Baltrunas, B. Ludwig, and F. Ricci. Context relevance assessment for recommender systems. In Proc. IUI '11, pages 287--290, 2011. Google ScholarDigital Library
- P. G. Campos, F. Dıez, and I. Cantador. Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. UMUAI, 24(1--2):67--119, 2014. Google ScholarDigital Library
- P. Cremonesi, F. Garzotto, S. Negro, A. Papadopoulos, and R. Turrin. Looking for "good" recommendations: A comparative evaluation of recommender systems. In Proc. Interact '11, pages 152--168, 2011. Google ScholarDigital Library
- P. Cremonesi, F. Garzotto, and R. Turrin. Investigating the persuasion potential of recommender systems from a quality perspective: An empirical study. ACM TIST, 2(2):11:1--11:41, 2012. Google ScholarDigital Library
- P. Cremonesi, Y. Koren, and R. Turrin. Performance of algorithms on top-n recommendation tasks. In Proc. RecSys '10, pages 39--46, 2010. Google ScholarDigital Library
- M. B. Dias, D. Locher, M. Li, W. El-Deredy, and P. J. Lisboa. The value of personalised recommender systems to e-business: A case study. In Proc. RecSys '08, pages 291--294, 2008. Google ScholarDigital Library
- N. Hariri, B. Mobasher, and R. Burke. Context adaptation in interactive recommender systems. In Proc. RecSys '14, pages 41--48, 2014. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM TOIS, 22(1):5--53, 2004. Google ScholarDigital Library
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proc. ICDM '08, pages 263--272, 2008. Google ScholarDigital Library
- D. Jannach and K. Hegelich. A case study on the effectiveness of recommendations in the mobile internet. In Proc. RecSys '09, pages 205--208, 2009. Google ScholarDigital Library
- D. Jannach, L. Lerche, F. Gedikli, and G. Bonnin. What recommenders recommend - an analysis of accuracy, popularity, and sales diversity effects. In Proc. UMAP '13, pages 25--37, 2013.Google ScholarCross Ref
- D. Jannach, M. Zanker, M. Ge, and M. Gröning. Recommender systems in computer science and information systems - a landscape of research. In Proc. EC-Web '12, pages 76--87, 2012.Google ScholarCross Ref
- E. Kirshenbaum, G. Forman, and M. Dugan. A live comparison of methods for personalized article recommendation at Forbes.com. In Proc. ECML/PKDD '12, pages 51--66, 2012. Google ScholarDigital Library
- Y. Koren. Collaborative filtering with temporal dynamics. In Proc. KDD '09, pages 447--456, 2009. Google ScholarDigital Library
- L. Lerche and D. Jannach. Using graded implicit feedback for bayesian personalized ranking. In Proc. RecSys '14, pages 353--356, 2014. Google ScholarDigital Library
- J. Liu, P. Dolan, and E. R. Pedersen. Personalized news recommendation based on click behavior. In Proc. IUI '10, pages 31--40, 2010. Google ScholarDigital Library
- B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Using sequential and non-sequential patterns in predictive web usage mining tasks. In Proc. ICDM '02, pages 669--672, 2002. Google ScholarDigital Library
- Q. N. Nguyen and F. Ricci. Long-term and session-specific user preferences in a mobile recommender system. In Proc. IUI '08, pages 381--384, 2008. Google ScholarDigital Library
- S. Rendle. Factorization machines with libFM. ACM Transactions on Intelligent Systems Technology, 3(3):57:1--57:22, 2012. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In Proc. UAI '09, pages 452--461, 2009. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In Proc.WWW '10, pages 811--820. ACM, 2010. Google ScholarDigital Library
- F. Ricci, A. Venturini, D. Cavada, N. Mirzadeh, D. Blaas, and M. Nones. Product recommendation with interactive query management and twofold similarity. In Proc. ICCBR '03, pages 479--493, 2003. Google ScholarDigital Library
- A. Said, D. Tikk, K. Stumpf, Y. Shi, M. Larson, and P. Cremonesi. Recommender systems evaluation: A 3D benchmark. In Proc. RUE WS at RecSys '12, pages 21--23, 2012.Google Scholar
- E. Shen, H. Lieberman, and F. Lam. What am I gonna wear?: Scenario-oriented recommendation. In Proc. IUI '07, pages 365--368, 2007. Google ScholarDigital Library
- Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, and A. Hanjalic. xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance. In Proc. RecSys '13, pages 431--434, 2013. Google ScholarDigital Library
- M. Tavakol and U. Brefeld. Factored MDPs for detecting topics of user sessions. In Proc. RecSys '14, pages 33--40, 2014. Google ScholarDigital Library
- M. Zanker, M. Bricman, S. Gordea, D. Jannach, and M. Jessenitschnig. Persuasive online-selling in quality & taste domains. In Proc. EC-Web '06, pages 51--60, 2006. Google ScholarDigital Library
Index Terms
- Adaptation and Evaluation of Recommendations for Short-term Shopping Goals
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