Enriching Sequential Recommendations with Contextual Auxiliary Information

Recommender Systems (RS) play a key role in offering suggestions and predicting items for users on e-commerce and social media platforms. Sequential recommendation systems (SRS) leverage the user’s previous interaction history to forecast the next user-item interaction. Although deep learning methods like CNNs and RNNs have enhanced recommendation quality, current models still face challenges in accurately predicting future items based on a user’s past behavior. Transformer-based SRS have shown a significant performance boost in generating accurate recommendations by using only item identifiers which are not sufficient to generate meaningful and relevant results. These models can be improved by incorporating descriptive features of the items, such as textual descriptions. This paper proposes a transformer-based SRS, ConSRec, Contextual Sequential Recommendations, that incorporates auxiliary information of the items, such as textual features, along with item identifiers to model user behavior sequences for producing more accurate recommendations. ConSRec builds upon the BERT4Rec model by integrating auxiliary information through sentence representations derived from the textual features of items. Extensive experiments conducted on several benchmark datasets demonstrate substantial improvements compared to other advanced models.

@article{Alkhalil2024,
  title     = {Enriching Sequential Recommendations with Contextual Auxiliary Information},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Adel Alkhalil},
  doi       = {10.14569/IJACSA.2024.0151283},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151283}
}