Abstract
Recommendation systems are widely used on Internet platforms and the Recommendation system is one of the most mature scenarios for data mining applications. Ranking algorithms in recommender systems are the most effective means to increase user clicks. At present, the recommendation system ranking algorithm is mainly divided into two categories. One is based on data mining, such as user features, product features, and scene features and crosses these functions manually or automatically. The second method, by mining the user’s behavior sequence, obtains the user’s interests and displays the product to the user. The first approach does not mine useful behavioral data. The second method cannot effectively perform feature crossover and loses some crossover information between users and products. This paper proposes a new ranking method for recommender systems. Feature Cross and User Interest Network FCI. It can efficiently and automatically discover cross features based on users, products, and scenarios. At the same time, mining the historical behavior information of users. In this algorithm, firstly our put-forward model resembles a wide-depth structure. Wide structure puts feature intersection network, deep structure puts user historical behavior mining network. Secondly experimental comparisons are made by changing different feature intersection networks and user behavior sequence networks. Finally, by adding the pre-training vector of the product, compare different pre-training methods. This approach greatly improves the CTR in practical recommender systems.
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Lei, D. (2022). FCI: Feature Cross and User Interest Network. In: Li, T., et al. Big Data. BigData 2022. Communications in Computer and Information Science, vol 1709. Springer, Singapore. https://doi.org/10.1007/978-981-19-8331-3_8
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DOI: https://doi.org/10.1007/978-981-19-8331-3_8
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