Ludewig, Malte2020-12-032020-12-032020http://hdl.handle.net/2003/3982210.17877/DE290R-21713As of today, personalized item suggestions provided by an automated recommender system have become a crucial part of many online services, e.g., online shops or media streaming applications, and extensive evidence exists that such systems increase both the user experience as well as the revenue of the providers. In academia, the recommendation problem is often framed as finding suitable items that a user is not yet aware of based on his long-term preference profile. In the real world, however, this problem formulation has a number of problems. Long-term profiles, e.g., are not available for new or anonymous users and recommendations can then only be based on the few most recent interactions in an ongoing usage session. Various approaches to this highly relevant setting of session-based recommendation that recently emerged in the research community were proposed over the recent years. However, in terms of the evaluation procedure, no common standard has been established so far. In this thesis, the author, therefore, proposes a publicly available framework for reproducible research and, furthermore, fairly compares many approaches, of which some were proposed by himself. Extensive experiments and a user study surprisingly showed that comparably simple nearest-neighbor techniques usually outperform recent deep learning models across many domains, datasets, and metrics. Even if long-term preferences are available for the users, recent works indicated that it might still be beneficial to consider the ongoing session, e.g., because a user started the session with a specific intent in mind. The author of this thesis, thus, conducted a systematic statistical analysis to assess what helps recommendations in being effective in such a session-aware scenario. This analysis is based on log data from a fashion retailer and insights were, furthermore, operationalized into novel session-aware recommendation approaches. Matching items of the customer’s ongoing session, reminding him of previously inspected clothes, recommending discounted items, and considering recent trends in the community showed to be particularly effective strategies, not only for item-item recommendation but also in the related scenario of search personalization.enRecommender systemsSession-based recommendationSession-aware recommendationNearest-neighborsNeural networks004doctoral thesisEmpfehlungssystemNächste-Nachbarn-ProblemNeuronales Netz