InteraRec: Interactive Recommendations Using Multimodal Large Language Models
arxiv(2024)
摘要
Weblogs, comprised of records detailing user activities on any website, offer
valuable insights into user preferences, behavior, and interests. Numerous
recommendation algorithms, employing strategies such as collaborative
filtering, content-based filtering, and hybrid methods, leverage the data mined
through these weblogs to provide personalized recommendations to users. Despite
the abundance of information available in these weblogs, identifying and
extracting pertinent information and key features necessitates extensive
engineering endeavors. The intricate nature of the data also poses a challenge
for interpretation, especially for non-experts. In this study, we introduce a
sophisticated and interactive recommendation framework denoted as InteraRec,
which diverges from conventional approaches that exclusively depend on weblogs
for recommendation generation. This framework captures high-frequency
screenshots of web pages as users navigate through a website. Leveraging
state-of-the-art multimodal large language models (MLLMs), it extracts valuable
insights into user preferences from these screenshots by generating a user
behavioral summary based on predefined keywords. Subsequently, this summary is
utilized as input to an LLM-integrated optimization setup to generate tailored
recommendations. Through our experiments, we demonstrate the effectiveness of
InteraRec in providing users with valuable and personalized offerings.
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