Music listening preferences at a given time depend on a wide range of contextual factors, such as user emotional state, location and activity at listening time, the day of the week, the time of the day, etc. It is therefore of great importance to take them into account when recommending music. However, it is very difficult to develop context-aware recommender systems that consider these factors, both because of the difficulty of detecting some of them, such as emotional state, and because of the drawbacks derived from the inclusion of many factors, such as sparsity problems in contextual pre-filtering. This work involves the proposal of a method for the detection of the user contextual state when listening to music based on the social tags of music items. The intrinsic characteristics of social tagging that allow for the description of items in multiple dimensions can be exploited to capture many contextual dimensions in the user listening sessions. The embeddings of the tags of the first items played in each session are used to represent the context of that session. Recommendations are then generated based on both user preferences and the similarity of the items computed from tag embeddings. Social tags have been used extensively in many recommender systems, however, to our knowledge, they have been hardly used to dynamically infer contextual states.
翻译:因此,在推荐音乐时,必须考虑到这些因素。然而,很难开发符合背景的建议系统来考虑这些因素,因为很难发现这些因素中的某些因素,例如情绪状态,而且由于列入许多因素,例如背景预过滤过程中的松散问题,因此,工作涉及提议一种方法,在根据音乐项目的社会标记监听音乐时,检测用户的背景状态。社会标记的内在特征允许对项目进行多维描述,可加以利用,以捕捉用户监听课中的许多背景层面。在每届会议上播放的第一批物品的标签的嵌入都用来代表会议的背景。然后,根据用户偏好和从标签嵌入中计算的项目的相似性,提出建议。社会标记在许多动态的系统里被广泛使用,但是,在动态背景系统中我们几乎无法使用。