Avinash Akella

Ruixuan Sun, Xinyi Li, Avinash Akella et al.

This paper explores the effectiveness of using large language models (LLMs) for personalized movie recommendations from users' perspectives in an online field experiment. Our study involves a combination of between-subject prompt and historic consumption assessments, along with within-subject recommendation scenario evaluations. By examining conversation and survey response data from 160 active users, we find that LLMs offer strong recommendation explainability but lack overall personalization, diversity, and user trust. Our results also indicate that different personalized prompting techniques do not significantly affect user-perceived recommendation quality, but the number of movies a user has watched plays a more significant role. Furthermore, LLMs show a greater ability to recommend lesser-known or niche movies. Through qualitative analysis, we identify key conversational patterns linked to positive and negative user interaction experiences and conclude that providing personal context and examples is crucial for obtaining high-quality recommendations from LLMs.

Ruixuan Sun, Xinyi Wu, Avinash Akella et al.

In the past decade, deep learning (DL) models have gained prominence for their exceptional accuracy on benchmark datasets in recommender systems (RecSys). However, their evaluation has primarily relied on offline metrics, overlooking direct user perception and experience. To address this gap, we conduct a human-centric evaluation case study of four leading DL-RecSys models in the movie domain. We test how different DL-RecSys models perform in personalized recommendation generation by conducting survey study with 445 real active users. We find some DL-RecSys models to be superior in recommending novel and unexpected items and weaker in diversity, trustworthiness, transparency, accuracy, and overall user satisfaction compared to classic collaborative filtering (CF) methods. To further explain the reasons behind the underperformance, we apply a comprehensive path analysis. We discover that the lack of diversity and too much serendipity from DL models can negatively impact the consequent perceived transparency and personalization of recommendations. Such a path ultimately leads to lower summative user satisfaction. Qualitatively, we confirm with real user quotes that accuracy plus at least one other attribute is necessary to ensure a good user experience, while their demands for transparency and trust can not be neglected. Based on our findings, we discuss future human-centric DL-RecSys design and optimization strategies.