Deep sequential real estate recommendation approach for solving item cold start problem

Abstract

The item cold-start problem occurs when a recommendation system cannot recommend new items owing to record deficiencies and new listing omissions. When searching for real estate, users can register a concurrent interest in recent and prior projects. Thus, an approach to recommend cold-start and warm-start items simultaneously must be determined. Furthermore, unrequired membership and stop-by behavior cause real estate recommendations to have many cold-start and new users. This characteristic encourages the use of a content-based approach and a session-based recommendation system. Herein, we propose a real estate recommendation approach for solving the item cold-start problem with acceptable warm-start item recommendations in the many-cold-start-users scenario. We modify a session-based recommendation system and employ existing mechanisms to efficiently deal with sequential and context information for the next-interacted item's encoded attribute prediction. Subsequently, we use the nearest-neighbors approach using weighted cosine similarity to determine conforming candidates. We use Recall@K and MRR@K with the top-n recommendation to evaluate warm-start and cold-start item recommendations among different applied mechanisms and against the baselines. The results demonstrate the effectiveness of efficiently integrating the information and the difficulty in performing well in warm-start and cold-start item recommendations simultaneously. Our proposed approach illustrates the capability of solving the item cold-start problem while yielding promising results in both recommendations although neither result is the best. We believe that our approach provides a suitable compromise between both recommendations and that it will benefit recommendation tasks focusing on both recommendations.