Session 13: Side Information, Items structure and Relations

Date: Friday September 22, 11:15 AM – 12:35 PM (GMT+8)
Room: Hall 406CX
Session Chair: Iadh Ounis
Parallel with: Session 14: Multi-task Recommendation

• RESFull Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation
  by Zhen Gong (Shanghai Jiao Tong University), Xin Wu (Bytedance Inc.), Lei Chen (Bytedance Inc.), Zhenzhe Zheng (Shanghai Jiao Tong University), Shengjie Wang (Bytedance Inc.), Anran Xu (Shanghai Jiao Tong University), Chong Wang (Bytedance Inc.) and Fan Wu (Shanghai Jiao Tong University).

  End-to-end retrieval models, such as Tree-based Models (TDM) and Deep Retrieval (DR), have attracted a lot of attention, but they are flawed in cold-start and long-tail item recommendation scenarios. Specifically, DR learns a compact indexing structure, enabling efficient and accurate retrieval for large recommendation systems. However, it is discovered that DR largely fails on retrieving cold-start and long-tail items. This is because DR only utilizes user-item interaction data, which is rare and often noisy for cold-start and long-tail items. And the end-to-end retrieval models are unable to make use of the rich item content features. To address this issue while maintaining the efficiency of DR indexing structure, we propose Full Index Deep Retrieval (FIDR) that learns indices for the full corpus items, including cold-start and long-tail items. In addition to the original structure in DR (called User Structure in FIDR) that learns with user-item interaction data (e.g., clicks), we add an Item Structure to embed items directly based on item content features (e.g., categories). With joint efforts of User Structure and Item Structure, FIDR makes cold-start items retrievable and also improves the recommendation quality of long-tail items. To our best knowledge, FIDR is the first to solve the cold-start and long-tail recommendation problem for the end-to-end retrieval models. Through extensive experiments on three real-world datasets, we demonstrate that FIDR can effectively recommend cold-start and long-tail items and largely promote overall recommendation performance without sacrificing inference efficiency. According to the experiments, the recall of FIDR is improved by 8.8% ~ 11.9%, while the inference of FIDR is as efficient as DR.

  Full text in ACM Digital Library

• RESSPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation
  by Andreas Peintner (Universität Innsbruck), Amir Reza Mohammadi (Universität Innsbruck) and Eva Zangerle (Universität Innsbruck).

  Session-based recommendation aims to predict the next item based on a set of anonymous sessions. Capturing user intent from a short interaction sequence imposes a variety of challenges since no user profiles are available and interaction data is naturally sparse. Recent approaches relying on graph neural networks (GNNs) for session-based recommendation use global item relations to explore collaborative information from different sessions. These methods capture the topological structure of the graph and rely on multi-hop information aggregation in GNNs to exchange information along edges. Consequently, graph-based models suffer from noisy item relations in the training data and introduce high complexity for large item catalogs. We propose to explicitly model the multi-hop information aggregation mechanism over multiple layers via shortest-path edges based on knowledge from the sequential recommendation domain. Our approach does not require multiple layers to exchange information and ignores unreliable item-item relations. Furthermore, to address inherent data sparsity, we are the first to apply supervised contrastive learning by mining data-driven positive and hard negative item samples from the training data. Extensive experiments on three different datasets show that the proposed approach outperforms almost all of the state-of-the-art methods.

  Full text in ACM Digital Library

• INDAccelerating Creator Audience Building through Centralized Exploration
  by Buket Baran (Spotify), Guilherme Dinis Junior (Spotify), Antonina Danylenko (Spotify), Olayinka S. Folorunso (Spotify), Gösta Forsum (Spotify), Maksym Lefarov (Spotify), Lucas Maystre (Spotify) and Yu Zhao (Spotify).

  On Spotify, multiple recommender systems enable personalized user experiences across a wide range of product features. These systems are owned by different teams and serve different goals, but all of these systems need to explore and learn about new content as it appears on the platform. In this work, we describe ongoing efforts at Spotify to develop an efficient solution to this problem, by centralizing content exploration and providing signals to existing, decentralized recommendation systems (a.k.a. exploitation systems). We take a creator-centric perspective, and argue that this approach can dramatically reduce the time it takes for new content to reach its full potential.

  Full text in ACM Digital Library

• INDBeyond Labels: Leveraging Deep Learning and LLMs for Content Metadata
  by Saurabh Agrawal (Tubi), John Trenkle (Tubi) and Jaya Kawale (Tubi).

  Content metadata plays a very important role in movie recommender systems as it provides valuable information about various aspects of a movie such as genre, cast, plot synopsis, box office summary, etc. Analyzing the metadata can help understand the user preferences and generate personalized recommendations catering to the niche tastes of the users. It can also help with content cold starting when the recommender system has little or no interaction data available to perform collaborative filtering. In this talk, we will focus on one particular type of metadata – genre labels. Genre labels associated with a movie or a TV series such as “horror” or “comedy” or “romance” help categorize a collection of movies into different themes and correspondingly setting up the audience expectation for a title. We present some of the challenges associated with using genre label information via traditional methods and propose a new way of examining the genre information that we call as the Genre Spectrum. The Genre Spectrum helps capture the various nuanced genres in a title and our offline and online experiments corroborate the effectiveness of the approach.

  Full text in ACM Digital Library

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