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

arXiv: https://arxiv.org/abs/2412.17310 · HTML: https://mlsanity.com/paper/2412.17310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models](https://mlsanity.com/paper/2002.06652/) (2020)
- [Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction](https://mlsanity.com/paper/2007.06434/) (2020)
- [Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation](https://mlsanity.com/paper/1607.07326/) (2016)
- [Personalized Bundle List Recommendation](https://mlsanity.com/paper/1904.01933/) (2019)
- [PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions](https://mlsanity.com/paper/2209.13015/) (2022)

## Abstract (arXiv)

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.