Evaluation of session-based recommendation algorithms
نویسندگان
چکیده
منابع مشابه
Evaluation of Session-based Recommendation Algorithms
Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the obs...
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A single ‘odd’ interaction can cause two user interaction sessions to diverge in similarity, and stand in the way of generalization. The sensitivity of session-based recommenders to session similarity motivates us to explicitly identify and remove such ‘similarity blockers’. Specifically, we leverage huge amounts of data, which allow us to identify blockers in the form of non-co-occurring items...
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Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users’ past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach b...
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Two effective neural network training algorithms are output weight optimization hidden weight optimization and conjugate gradient. The former performs better on correlated data, and the latter performs better on random data. Based on these observations and others, we develop a procedure to test general neural network training algorithms. Since good neural network algorithm should perform well...
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The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems—a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based Collaborative filtering is the most successful technology for building recommender systems to date, and is extensively used in many com...
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ژورنال
عنوان ژورنال: User Modeling and User-Adapted Interaction
سال: 2018
ISSN: 0924-1868,1573-1391
DOI: 10.1007/s11257-018-9209-6