Decision4rec improves recommendations using decision models and knowledge graphs
Decision4Rec: A Jev-Inspired Framework of Decision Models on Knowledge and Active Graphs for Multi-Agent Recommender Systems
Artificial IntelligenceComputation and LanguageInformation Retrieval
Summary
Recommendation systems try to guess what products or items you might like, but they often struggle with tricky scenarios or new items. The authors introduce Decision4Rec, a new framework that uses a special kind of model, called a decision model, to better choose recommendations by learning from past decisions expressed on a knowledge graph. This system combines proposals from several agents and then ranks the candidates, making the final choice more accurate, especially for sparse data. The approach works well on several product databases and even on new categories it wasn’t trained on.
What this means in practice
- •For ecommerce platform engineers: Enhance product recommendation accuracy in sparse catalogs by integrating decision models and knowledge graphs for improved ranking.
- •For streaming service developers: Improve content suggestions on dense user-item datasets using a decision model framework trained on varied recommendation scenarios.
Authors
Marco Polignano, Het Darshan Mehta, Pasquale Lops, Ernesto William De Luca, Giovanni Semeraro
Abstract
Decision models, such as the commercial Jev, are language models that return one probability for each option of a question instead of generating text. We present Decision4Rec, an open and modular framework that makes decision models useful for recommendation. A knowledge graph writes the logs of each recommendation scenario as short statements on a defined scale, such as "sold: top 10%". A pool of agents, proposes candidates, and a residual ranker keeps the best 30. A decision model, trained once on real choices of three scenarios, reads them through one interface of typed questions, and a fusion step combines its answers with the score of the ranker. Any model with this interface can be used, and we run the commercial Jev, the released open Kev and Kev models trained by us on recommendation requests. The framework with a trained 4B decision model is 25 to 36% above the best trained baseline in NDCG@10 in three sparse Amazon catalogues, one of them never used to train the decision model, and almost tie with baselines on the dense Steam dataset. Most of the gain comes from the pool, and with a trained recommender it comes from what the agents know about its candidates rather than from new candidates. The 27B decision model adds to the pool 5 to 6% above the 0.8B model in three of four domains. A trained 0.8B open model beats the released Jev in eleven of fifteen settings and is never worse. On MovieLens-1M, a dense domain never seen in training, the gain is 5 to 7%. Code, configurations and trained models are available at https://github.com/marcopoli/decision4rec and https://huggingface.co/m-polignano.