%0 Conference Paper %1 Zhang_2014 %A Zhang, Yongfeng %A Lai, Guokun %A Zhang, Min %A Zhang, Yi %A Liu, Yiqun %A Ma, Shaoping %B Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval %D 2014 %I ACM %K explanation %P 83-92 %R 10.1145/2600428.2609579 %T Explicit factor models for explainable recommendation based on phrase-level sentiment analysis %U https://doi.org/10.1145%2F2600428.2609579 %X Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations.

@inproceedings{Zhang_2014, abstract = {Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations.}, added-at = {2021-03-10T23:23:22.000+0100}, author = {Zhang, Yongfeng and Lai, Guokun and Zhang, Min and Zhang, Yi and Liu, Yiqun and Ma, Shaoping}, biburl = {https://www.bibsonomy.org/bibtex/2bc17c0f4e47836616532d9b93f78b8ac/brusilovsky}, booktitle = {Proceedings of the 37th international {ACM} {SIGIR} conference on Research {\&} development in information retrieval}, description = {Explicit factor models for explainable recommendation based on phrase-level sentiment analysis | Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval}, doi = {10.1145/2600428.2609579}, interhash = {d205aa88dfbff6a914c09bcbe5f194a7}, intrahash = {bc17c0f4e47836616532d9b93f78b8ac}, keywords = {explanation}, month = jul, pages = {83-92}, publisher = {{ACM}}, timestamp = {2021-03-10T23:23:22.000+0100}, title = {Explicit factor models for explainable recommendation based on phrase-level sentiment analysis}, url = {https://doi.org/10.1145%2F2600428.2609579}, year = 2014 }