Abstract
Watching movies is one of the social activities typically done in groups. Emotion is the most vital factor that affects movie viewers' preferences. So, the emotional aspect of the movie needs to be determined and analyzed for further recommendations. It can be challenging to choose a movie that appeals to the emotions of a diverse group. Reaching an agreement for a group can be difficult due to the various genres and choices. This paper proposes a novel approach to group movie suggestions by examining emotions from three different channels: movie descriptions (text), soundtracks (audio), and posters (image). We employ the Jaccard similarity index to match each participant's emotional preferences to prospective movie choices, followed by a fuzzy inference technique to determine group consensus. We use a weighted integration process for the fusion of emotion scores from diverse data types. Then, group movie recommendation is based on prevailing emotions and viewers' best-loved movies. After determining the recommendations, the group's consensus level is calculated using a fuzzy inference system, taking participants' feedback as input. Participants (n=130) in the survey were provided with different emotion categories and asked to select the emotions best suited for particular movies (n=12). Comparison results between predicted and actual scores demonstrate the efficiency of using emotion detection for this problem (Jaccard similarity index = 0.76). We explored the relationship between induced emotions and movie popularity as an additional experiment, analyzing emotion distribution in 100 popular movies from the TMDB database. Such systems can potentially improve the accuracy of movie recommendation systems and achieve a high level of consensus among participants with diverse preferences.
Abstract (translated)
观看电影是人们在集体活动中通常会进行的一种活动。情感是影响电影观众偏好的最至关重要的因素。因此，电影的情感方面需要进行确定和分析，为进一步建议提供依据。选择一部能引起观众情感共鸣的电影可能会具有挑战性。由于各种流派和选择，达成 group 一致意见可能很难。本文提出了一种通过研究电影描述（文本）、音乐（音频）和海报（图像）中的情感来提出新的群体电影建议的方法。我们使用 Jaccard 相似性指数将每个参与者的情感偏好与潜在电影选择匹配，然后使用模糊推理技术确定群体共识。我们使用加权集成过程对不同数据类型的情感分数进行融合。然后，群体电影推荐是基于当前情感和观众最喜欢的电影。在确定推荐后，使用模糊推理系统计算群体共识水平，以输入参与者的反馈。调查中的参与者（n=130）被提供了不同的情感类别，并被要求选择最适合特定电影的情感（n=12）。预测和实际得分的比较结果证明了使用情感检测解决这个问题（Jaccard 相似性指数 = 0.76）的有效性。我们还研究了诱导情感与电影流行程度之间的关系，作为另一个实验，分析了来自 TMDB 数据库中100部热门电影的情感分布。这样的系统可以有潜力提高电影推荐系统的准确性，并在具有不同偏好的参与者之间实现高水平的共识。
URL
https://arxiv.org/abs/2404.13778
