Recommendation algorithm considering complementation, substitution relation and sequence pattern of goods

doi:10.3969/j.issn.1000-1565.2024.05.012

Abstract

Abstract: In the real purchasing scenario of users, shopping is not only based on interests, but also on current and future needs. However, most of the existing recommendation methods focus on mining users recent interests, and rarely study users potential needs from the relationship between products. In order to improve the accuracy of the recommendation algorithm and enrich the types of recommendation, this paper integrates the characteristics of commodity complementary substitution relationship and purchase sequence pattern into the recommendation algorithm, and proposes a recommendation algorithm that considers the commodity complementary substitution relationship and purchase sequence pattern to study the potential needs of users. The algorithm is verified on Amazon public data set Grocery. Compared with the relevant algorithms, the results show that the proposed algorithm is effectively improved in hits Ratio HR(hits ratio)and normalized discounted cumulative gain(NDCG).

Key words: complementary pairing, sequence mode, personalized recommendations

CLC Number:

REN Zhibo,RONG Xiuling,SONG Xinxin. Recommendation algorithm considering complementation, substitution relation and sequence pattern of goods[J]. Journal of Hebei University(Natural Science Edition), 2024, 44(5): 551-560.

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