Investigating deciding factors of product recommendation in social media

Jou Yu Chen, Ping Yu Hsu, Ming Shien Cheng, Hong Tsuen Lei, Shih Hsiang Huang, Yen Huei Ko, Chen Wan Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


With the growing popularity of social media, the number of people using social media to communicate and interact with others has increased steadily. As a result, social commerce has become a new phenomenon. In the past, most of the product recommendations in microblogging only dealt with personal preferences and interests, and ignored other possible factors such as Crowd Interest, Popularity of Products, Reputation of Creators, Types of Preference and Recent. Nowadays, these variables used by Facebook to recommend posts to their users. Therefore, this research adapted those five aspects and analyzed their effectiveness to recommend products on social media. This study used the Plurk API to develop and implement recommended robots that recommend products at specific times of the day so that they can get product information and meet recommended tasks in the social circle. The empirical results showed that the Interest, Popularity and Type have significant impacts on recommendation effectiveness.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings
EditorsYing Tan, Qirong Tang, Yuhui Shi
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319938172
StatePublished - 2018
Event9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, China
Duration: 17 Jun 201822 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10942 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Swarm Intelligence, ICSI 2018


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