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Recommendation system plays an important role in online web applications.Sequential recommender further models user short-term preference throughexploiting information from latest user-item interaction history. Most of thesequential recommendation methods neglect the importance of ever-changing itempopularity. We propose the model from the intuition that items with most userinteractions may be popular in the past but could go out of fashion in recentdays. To this end, this paper proposes a novel sequential recommendationapproach dubbed TRec, TRec learns item trend information from implicit userinteraction history and incorporates item trend information into next itemrecommendation tasks. Then a self-attention mechanism is used to learn betternode representation. Our model is trained via pairwise rank-based optimization.We conduct extensive experiments with seven baseline methods on four benchmarkdatasets, The empirical result shows our approach outperforms otherstateof-the-art methods while maintains a superiorly low runtime cost. Ourstudy demonstrates the importance of item trend information in recommendationsystem designs, and our method also possesses great efficiency which enables itto be practical in real-world scenarios.

TRec: Sequential Recommender Based On Latent Item Trend Information