Popularity Estimation and New Bundle Generation using Content and Context based Embeddings

• URL: http://arxiv.org/abs/2412.17310v1
• Date: Mon, 23 Dec 2024 06:04:14 GMT
• Title: Popularity Estimation and New Bundle Generation using Content and Context based Embeddings
• Authors: Ashutosh Nayak, Prajwal NJ, Sameeksha Keshav, Kavitha S. N., Roja Reddy, Rajasekhara Reddy Duvvuru Muni,
• Abstract summary: Product bundling is an exciting development in the field of product recommendations. We introduce new bundle popularity metrics based on sales, consumer experience and item diversity in a bundle. Our experiments indicate that we can generate new bundles that can outperform the existing bundles on the popularity metrics by 32% - 44%.
• Score: 1.099532646524593
• License:
• Abstract: Recommender systems create enormous value for businesses and their consumers. They increase revenue for businesses while improving the consumer experience by recommending relevant products amidst huge product base. Product bundling is an exciting development in the field of product recommendations. It aims at generating new bundles and recommending exciting and relevant bundles to their consumers. Unlike traditional recommender systems that recommend single items to consumers, product bundling aims at targeting a bundle, or a set of items, to the consumers. While bundle recommendation has attracted significant research interest recently, extant literature on bundle generation is scarce. Moreover, metrics to identify if a bundle is popular or not is not well studied. In this work, we aim to fulfill this gap by introducing new bundle popularity metrics based on sales, consumer experience and item diversity in a bundle. We use these metrics in the methodology proposed in this paper to generate new bundles for mobile games using content aware and context aware embeddings. We use opensource Steam Games dataset for our analysis. Our experiments indicate that we can generate new bundles that can outperform the existing bundles on the popularity metrics by 32% - 44%. Our experiments are computationally efficient and the proposed methodology is generic that can be extended to other bundling problems e.g. product bundling, music bundling.

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