Abstract
Recommender system is playing an indispensable role in our daily lives as well as in the Internet industry for the problem of information overload. Similarity search is a crucial operation in networks, database and Web search, which is usually used in recommendation. With the advent of the era of mass information that consists of great time span data, it is important to study similarity search combining temporal information. Intuitively, the older the data is, the less time weight will be in similarity calculation, so the conventional research always use the forgetting curve to model the time factor. However, these tasks only take time as a common attribute rather than a dimension. Each interaction actually is not independent in the time dimension and their chronological order contains a lot of information, utilizing the symbolic sequence relationship among the interaction and the overall structure of the data network will use these contexts efficiently and benefit the measure precision of similarity search. In this paper, a recommendation framework called SeqSim is proposed, which can synthetically utilizes the interaction information and its chronological order information to measure sequence similarity, then measures the item similarity based on sequence similarity and finally makes recommendations by preference curve. Empirical studies on real-world have shown that our new algorithm substantially improves the precision of measuring the similarity and meet the special requirements of the similarity calculation in more applications.
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Yang, G., Hong, X., Peng, Z. (2020). Recommendation with Temporal Dynamics Based on Sequence Similarity Search. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_47
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DOI: https://doi.org/10.1007/978-3-030-60239-0_47
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