In this paper, we compare the performance of item-based and user-based recommendation algorithms as well as propose an ensemble that combines both systems. We investigate the effect of applying LSA, as well as varying the neighbourhood size on the different algorithms. Finally, we experiment with...
This work introduces a single approach to constructing recommendation systems based on content or based on collaborative filtering using modal variables for users and items. In the content-based system, user profiles and item profiles are created from modal representations of their features, and a ...
In the first one, we consider three approaches to music recommendation, two of them based on a semantic music similarity measure, and one based on a semantic probabilistic model. In the second application, we address the visualization of the user's musical preferences by creating a humanoid ...
Nowadays large portion of web-based businesses, research projects, and scientist use recommendation systems to help their business to thrive & flourish. Standard recommendation system utilises either user CF, item CF or content based recommendation system, these furthermore confront issues like item col...
展开 关键词: collaborative annotation cultural heritage documents episodes of actions recommender system trace-based reasoning 会议名称: International Conference on Case-based Reasoning Research & Development 会议时间: 07/19/2010 主办单位: Springer-Verlag 被...
recommendation.In addition,the users' preference on item attribute instead of rating score was used to recommend the new items.The experimental results based on MovieLens data set show that the improved algorithm can solve the problem of cold-start and improve the accuracy of system recommendation ...
Summary: Build a content-based recommendation algorithm. Priority: 1 Stories: As a customer, I want to see recommendations based on my prior purchases so I can find the best items to buy. Test: The algorithm returns three content-based recommendations for a customer’s user ID. Estimate: ...
However, in every recommendation, some individuals might not be happy with the recommendation. For example, the Fairness Strategy (a social choice-based aggregation strategy) (Masthoff 2004) might recommend an item that one or more group members do not like but will recommend other items that ...
Wei, Y., Wei, G., Wu, S. (2022). News Recommendation Method Based on Topic Extraction and User Interest Transfer. In: Shi, X., Bohács, G., Ma, Y., Gong, D., Shang, X. (eds) LISS 2021. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978...
对于level-1的intent unit,初始化即为item的embedding,对于更高level的intent unit,初始化采用两种方法相加的方式:一种是采用平均池化,最大池化等方法;一种是采用GRU来提取order-sensitive intent。初始化的level-k的intent unit表示为e^k_j.对于会话图的构建,根据intend unit在原始会话中的邻接关系,来构建相同level...