Sequence-Aware Recommender Systems (1802.08452v1)

Abstract: Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.

• The paper categorizes recommendation tasks into context adaptation, trend detection, repeated recommendations, and order constraints.
• It surveys sequence learning algorithms like Markov models, RNNs, and hybrid methods for capturing dynamic user interactions.
• It highlights realistic evaluation protocols and outlines future directions to integrate short- and long-term user preferences.

Sequence-Aware Recommender Systems: A Survey

The paper focuses on sequence-aware recommender systems, a domain within recommender systems where user-item interactions over time are analyzed to enhance personalization. Traditionally, recommenders have been rooted in the matrix completion problem—capturing user preferences through a user-item matrix intended for missing value prediction. However, these systems often overlook the intricacies of user interactions and behaviors that occur sequentially over time.

Overview

Sequence-aware recommender systems aim to leverage user interaction logs that are sequentially ordered, capturing the dynamics of user preferences as they evolve over time. The paper categorizes sequence-aware recommendation tasks and examines existing algorithmic approaches while identifying open challenges.

Categorization

The survey categorizes sequence-aware recommendation tasks into four primary goals:

• Context Adaptation: Recommendations are tailored to users' short-term interests, as inferred from recent interactions.
• Trend Detection: Identifying both community-wide and individual trends to improve recommendations.
• Repeated Recommendation: Recommending previously used or purchased items, either as reminders or due to repeated behavior patterns.
• Order Constraints and Sequential Patterns: Utilizing strict or weak order constraints to improve recommendation quality, particularly in domains requiring logical item sequences.

Algorithmic Approaches

The survey identifies several classes of algorithms used in sequence-aware recommenders:

• Sequence Learning: Involves the use of frequent pattern mining, Markov models, reinforcement learning, and recurrent neural networks (RNNs) to model user interaction sequences.
• Matrix Factorization: Although traditionally used for matrix completion, this technique is adapted for sequence consideration by altering input datasets and loss functions.
• Hybrid Methods: Combines sequence learning with matrix factorization or clustering methods for robust modeling.
• Others: Graph-based methods and discrete optimization methods are also explored, catering to specific application constraints like course sequencing or playlist generation.

Evaluation and Results

Evaluation methodologies are critical for assessing sequence-aware recommender systems. The paper examines data partitioning strategies, including session-based splits, and evaluation metrics like precision and recall adapted for sequence prediction. It highlights the importance of realistic evaluation protocols to reflect practical use cases accurately.

Implications and Future Directions

The implications of adopting sequence-aware recommenders are extensive, particularly enhancing the adaptability of recommendations to the user's current context. This approach can improve user experience across domains like e-commerce, streaming media, and POI services.

Looking forward, it is imperative to develop more integrated models that can harness short-term and long-term user preferences. Moreover, leveraging additional data types such as search behaviors or navigation patterns, and detecting broader trends within user communities, presents new opportunities. Lastly, the development of more standardized evaluation methodologies, purposefully aligned with real-world applications, will better facilitate the advancement of this research area.

In summary, the paper presents a comprehensive overview of sequence-aware recommender systems, identifying current practices, challenges, and potential future directions in the field. This survey underscores the growing importance of temporal data in delivering refined and contextually appropriate recommendations.