Deep Interest-Shifting Network with Meta-Embeddings for Fresh Item Recommendation

Abstract EN

Nowadays, people have an increasing interest in fresh products such as new shoes and cosmetics.

To this end, an E-commerce platform Taobao launched a fresh-item hub page on the recommender system, with which customers can freely and exclusively explore and purchase fresh items, namely, the New Tendency page.

In this work, we make a first attempt to tackle the fresh-item recommendation task with two major challenges.

First, a fresh-item recommendation scenario usually faces the challenge that the training data are highly deficient due to low page views.

In this paper, we propose a deep interest-shifting network (DisNet), which transfers knowledge from a huge number of auxiliary data and then shifts user interests with contextual information.

Furthermore, three interpretable interest-shifting operators are introduced.

Second, since the items are fresh, many of them have never been exposed to users, leading to a severe cold-start problem.

Though this problem can be alleviated by knowledge transfer, we further babysit these fully cold-start items by a relational meta-Id-embedding generator (RM-IdEG).

Specifically, it trains the item id embeddings in a learning-to-learn manner and integrates relational information for better embedding performance.

We conducted comprehensive experiments on both synthetic datasets as well as a real-world dataset.

Both DisNet and RM-IdEG significantly outperform state-of-the-art approaches, respectively.

Empirical results clearly verify the effectiveness of the proposed techniques, which are arguably promising and scalable in real-world applications.