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A Simple Yet Effective Approach for Improving Diversity in Session-Based Recommendation

Core Concepts
A simple and effective framework called DCA-SBRS that can be easily instantiated with existing representative accuracy-oriented session-based recommender systems to improve their diversity performance while preserving recommendation accuracy.
The paper proposes a Diversified Category-aware Attentive framework called DCA-SBRS that can be used as a plugin to improve the diversity performance of existing accuracy-oriented session-based recommender systems (SBRSs) without significantly deteriorating their recommendation accuracy. The key components of DCA-SBRS are: Model-agnostic Diversity-oriented Loss (MDL) function: This loss function works with the accuracy-oriented loss (e.g., cross-entropy loss) to enhance the diversity of the recommended list by leveraging the items' category information and the estimated item scores from the given SBRS. Non-invasive Category-aware Attention (NCA) mechanism: This mechanism utilizes the category information as directional guidance to replace the normal attention mechanism widely used in existing accuracy-oriented SBRSs. This helps maintain the recommendation accuracy while improving diversity. Extensive experiments on three real-world datasets show that DCA-SBRS can help existing SOTA SBRSs achieve extraordinary performance in terms of diversity (e.g., 74.1% increase on ILD@10) and comprehensive performance (e.g., 52.3% lift on F-score@10), without significantly deteriorating recommendation accuracy compared to SOTA diversified SBRSs.
The average session length in the Diginetica, Retailrocket, and Tmall datasets is 4.85, 3.53, and 6.08, respectively. The diversity score (DS) of the training set in the Diginetica, Retailrocket, and Tmall datasets is 0.3741, 0.4646, and 0.6575, respectively. The repeat ratio (RR) of the training set in the Diginetica, Retailrocket, and Tmall datasets is 0.1301, 0.2488, and 0, respectively.

Deeper Inquiries

How can the proposed DCA-SBRS framework be extended to other recommendation tasks beyond session-based recommendation

The proposed DCA-SBRS framework can be extended to other recommendation tasks beyond session-based recommendation by adapting the model-agnostic diversity-oriented loss function and the non-invasive category-aware attention mechanism to suit the specific characteristics of the new recommendation task. For instance, in a context where user preferences are more long-term or static, the framework can be modified to incorporate historical user data over a longer period. Additionally, for tasks where item relationships play a crucial role, the attention mechanism can be further refined to capture more complex item interactions. By customizing these components based on the requirements of the new recommendation task, the DCA-SBRS framework can be effectively applied to a wide range of recommendation scenarios.

What are the potential limitations of the standard comprehensive measure (F-score) in evaluating both accuracy and diversity, and how can it be improved

The standard comprehensive measure, F-score, may have limitations in evaluating both accuracy and diversity in recommendation systems. One potential limitation is that F-score combines accuracy and diversity metrics into a single value, which may not provide a clear understanding of the trade-off between the two aspects. To improve the evaluation, a more nuanced approach could involve separately assessing accuracy and diversity metrics and then combining them using a weighted average based on the importance of each aspect. This would provide a more detailed and informative evaluation of the recommendation system's performance in terms of both accuracy and diversity.

How can the category-aware attention mechanism be further enhanced to better capture the relationship between items and their categories

To enhance the category-aware attention mechanism to better capture the relationship between items and their categories, several improvements can be considered. One approach is to incorporate semantic information about the categories to provide more context for the attention mechanism. This could involve using pre-trained embeddings for the categories or leveraging external knowledge bases to enrich the category representations. Additionally, exploring more advanced fusion techniques, such as attention gating or hierarchical attention, could help the model better integrate category information into the recommendation process. By enhancing the category-aware attention mechanism in these ways, the model can more effectively capture the nuances of item-category relationships and improve recommendation performance.