Novel User Preference Recommender System Based on Twitter Profile Analysis

Research summary

The summary, key findings, methodology and relevance notes below are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.

The Problem

Conventional recommender systems lack interactive ways to adjust recommendation weights and ignore the interests users express on social media.

The Methodology

System design and experimental evaluation: mining and analysis of Twitter profiles and timelines on the IBM Watson platform to predict product and service categories, with a reported correlation between consumed categories and tweet content.

The Core Finding

A recommender built on IBM Watson that mines a user’s Twitter profile and timeline predicted the category of goods and services the user is most likely to consume, and the authors report a strong correlation between consumed categories and tweet content.

The Citation

Vajjhala, N. R., Rakshit, S., Oshogbunu, M., & Salisu, S. (2021). Novel User Preference Recommender System Based on Twitter Profile Analysis. In Samarjeet Borah, Ratika Pradhan, Nilanjan Dey, Phalguni Gupta (Eds.), Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020) (pp. 85–93). Springer Singapore. https://doi.org/10.1007/978-981-15-7394-1_7 Copy APA

What question does this paper answer?

Can a user’s Twitter profile and timeline be mined to infer their interests and recommend relevant products and services, addressing the lack of interactive ways to adjust recommendation weights in conventional recommender systems?

What did the study find, in detail?

The proposed system mines and analyses a user’s Twitter profile and tweets to identify interests and then recommends relevant products and services. Built on the IBM Watson AI platform, the experimental output displayed the category of goods and services the user is most likely to consume, and the authors report a strong correlation between the category of products and services a user consumes and their tweets.

Why does it matter?

Recommender systems help consumers make informed, personalised decisions, but most lack interactive control over algorithm weights. Using a person’s own public social media activity as the preference signal offers e-commerce services a cold-start-friendly source of personalisation.

Study at a glance

Abstract

Recommender systems can help provide preference-based personalized services to consumers and help them make informed decisions. However, a key shortcoming of the recommender systems is the lack of interactive methods to dynamically change the weights of recommendation algorithms. Our proposed system uses the Twitter profile and tweets to identify the interests of a user and then recommends the relevant products and services to that user. Our recommendation system is built to predict and personalize products and services based on the result of mining and analyzing the user’s Twitter timeline. The proposed recommender system is built upon an artificial intelligence platform called IBM Watson. The experimental result from the platform displayed the category of goods and services the user is most likely to consume. Our recommender system also showed a strong correlation between the category of products and services a user consumes and his/her tweets.

Abstract as published in Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020).

Keywords: Personalization; Collaborative; Recommender; E-commerce; Twitter; Mining; Hybrid; Retrieval

Limitations

• The abstract does not report the number of users, evaluation protocol or correlation statistics behind the reported relationship.
• Based on Twitter (now X) data and the IBM Watson platform as available at the time of the study.

When this research may be relevant

This paper may be relevant to researchers studying social-media-based recommender systems, user profiling from Twitter/X data, personalisation in e-commerce, hybrid recommendation approaches, and applications of the IBM Watson platform.

Research topics addressed: recommender systems; personalisation; Twitter data mining; social media analytics; user profiling; IBM Watson; e-commerce; hybrid recommendation; text mining; consumer preference prediction

How to cite

Vajjhala, N. R., Rakshit, S., Oshogbunu, M., & Salisu, S. (2021). Novel User Preference Recommender System Based on Twitter Profile Analysis. In Samarjeet Borah, Ratika Pradhan, Nilanjan Dey, Phalguni Gupta (Eds.), Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020) (pp. 85–93). Springer Singapore. https://doi.org/10.1007/978-981-15-7394-1_7

@inproceedings{vajjhala2021twitter,
  title = {Novel User Preference Recommender System Based on Twitter Profile Analysis},
  author = {Vajjhala, Narasimha Rao and Rakshit, Sandip and Oshogbunu, Michael and Salisu, Shafiu},
  booktitle = {Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020)},
  series = {Advances in Intelligent Systems and Computing},
  editor = {Samarjeet Borah and Ratika Pradhan and Nilanjan Dey and Phalguni Gupta},
  pages = {85--93},
  year = {2021},
  publisher = {Springer Singapore},
  doi = {10.1007/978-981-15-7394-1_7},
  url = {https://doi.org/10.1007/978-981-15-7394-1_7}
}