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In this paper, we propose a two-stage ranking approach for recommending linear TV programs. The proposed approach first leverages user viewing patterns regarding time and TV channels to identify potential candidates for recommendation and then further leverages user preferences to rank these candidates given textual information about programs. To evaluate the method, we conduct empirical studies on a real-world TV dataset, the results of which demonstrate the superior performance of our model in terms of both recommendation accuracy and time efficiency.
In this paper, we propose a novel optimization criterion that leverages features of the skew normal distribution to better model the problem of personalized recommendation. Specifically, the developed criterion borrows the concept and the flexibility
User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall interest. However, user interes
Cross domain recommendation (CDR) has been proposed to tackle the data sparsity problem in recommender systems. This paper focuses on a common scenario for CDR where different domains share the same set of users but no overlapping items. The majority
An effective email search engine can facilitate users search tasks and improve their communication efficiency. Users could have varied preferences on various ranking signals of an email, such as relevance and recency based on their tasks at hand and
News recommendation is critical for personalized news access. Existing news recommendation methods usually infer users personal interest based on their historical clicked news, and train the news recommendation models by predicting future news clicks