LLM Based Generation of Item-Description for Recommendation System

By Brijraj Singh, Research Scientist at Sony Research India

20th November 2023
Brijraj Singh summarises the paper titled ‘ LLM Based Generation of Item-Description for Recommendation System’ that was accepted at the RECSYS-23 conference in Singapore, this September.
In this blog, Brijraj Singh summarises the paper titled ‘LLM Based Generation of Item-Description for Recommendation System’ co-authored by Arkadeep Acharya and Onoe Naoyuki which was accepted at the Recommender Systems 2023 (RecSys) Conference in Singapore from 18th-22nd September 2023.


Recommendation systems play a significant role in keeping users engaged with platforms. The availability of services like media streaming, online gaming, e-commerce and more has raised the need for and importance of recommendation systems and is being applied on multiple domains involved in customer services. Multiple algorithms have been developed in the race to provide more relevant recommendations to users. Recall, Hit ratio, NDCG, CTR are popular metrics to judge the performance of the recommendation model, where the efficacy of the algorithm is validated with the user’s response. Many algorithms predict the rating of the items by a particular user, and RMSE is considered as their criteria. However, predicting the rating alone does not complete the task of recommendation, which also requires a further decision of selecting a few items out of good-rated items.
Figure: Process diagram of LLM based Recommendation System


In this paper we explore movie recommendation and have established the concept (CDHRNN: Content Driven Hierarchical Recurrent Neural Network) of considering the description of the movie along with movie_Id which provides better recommendation performance because of the better representation of the items. The movie_Id or user_Id does not contain any information (as they are unique numbers) that can help in understanding whether two movies are similar or dissimilar by looking at their Id. Therefore, when they are considered with the description, the previous query can easily be answered, which helps in improving the recommendation performance. In the case of the video recommendation, where the item is a movie, finding the description/plot of the movie is not so challenging as it can easily be web-scrapped through a repository like IMDb. However, there could be a scenario when the description of the items cannot be crawled (considering other recommendation domains as well), in such cases we have proposed the use of LLM (Large Language Models), which receives exhaustive training of existing popular content. To be specific, we used Alpaca-Lora, and with the help of prompting which sets the context to provide the desirable description or plot of the movie. Since, the performance of LLM depends on its encounter with the online content at the time of training, it sets up a condition where only contemporary plots should be requested of the movies. If we query the plot of an old and unpopular movie, there is a chance that the model would not have received the training of that content and then it will try to provide the description based on its learning corresponding to similar items, which might not be relevant. This problem of LLM is known as hallucination when it provides the response based on irrelevant understanding. Hence, LLM helps in generating the content under certain assumptions.

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