TIEN: Temporal interest-aware evolution model for “Next Item Recommendation”

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The “next item recommendation” is used to predict the next item that a user may be interested in and its main challenges is identifying the user's interests. The current state-of-the-art methods mostly adopt time-series-based recommendation methods but ignore the role of time intervals in the interest evolution process. This motivated us to propose a new Time Interest-aware Evolutionary Network model (TIEN) to predict the next item that a user may be interested in. TIEN highlights the role of time intervals in the interest evolution process and divides interests into long-term interests and short-term interests. In TIEN: (1) We devise a time interval-based attention network TAN to capture the user's interests, which employs item embeddings, behavior sequence embeddings, and time intervals to calculate the user's interest attention; (2) We devise an ATT-GRU network based on candidate activation vectors to capture the interest evolution process, which skillfully leverages the reset gate, hidden layer state, item information, time intervals, etc. to participate in interest evolution; (3) We introduce a dual-network collaborative training mechanism, where the obtained interests of users are treated as weights to guide the interest evolution process. Experimental results on multiple benchmark datasets show that TIEN has higher performance than the current state-of-the-art methods.