Contextual Video Recommendation by Multimodal Relevance and User Feedback
ACM Transactions on Information Systems (TOIS)
Integrating collaborative filtering and matching-based search for product recommendations
Journal of Theoretical and Applied Electronic Commerce Research
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It is important for online stores to improve Customer Lifetime Value (LTV) if they are to increase their profits. Conventional recommendation methods suggest items that best coincide with user's interests to maximize the purchase probability, and this does not necessarily help to improve LTV. We present a novel recommendation method that maximizes the probability of the LTV being improved, which can apply to both of measured and subscription services. Our method finds frequent purchase patterns among high LTV users, and recommends items for a new user that simulate the found patterns. Using survival analysis techniques, we efficiently extract information from log data to find the patterns. Furthermore, we infer a user's interests from purchase histories based on maximum entropy models, and use these interests to improve the recommendations. Since a higher LTV is the result of greater user satisfaction, our method benefits users as well as online stores. We evaluate our method using two sets of real log data for measured and subscription services.