Abstract
This paper presents a Slope one improved algorithm based on user similarity and user interest forgetting function. Aiming at the problem of large number of users and a lot of noise data, the inactive users are filtered out by setting the threshold of user activity, and then the neighbors of the target users are obtained through the calculation of user similarity. According to interest forgetting function, and then filter out items that have less effect on current users to reduce the noise data to improve the accuracy of the algorithm. Experimental comparison shows that the improved algorithm has better accuracy than the commonly used weighted Slope one and two-pole Slope one.
Highlights
The basic slope one algorithmThe basic method of the algorithm is to use a simple regression expression w=f(v)=v+b, where v is the prediction score of the user u for the project, Ru,j is the average deviation of itemi from the itemj score, denoted as Devi,j, U is defined as a user set, Si,j (U) is a set of users scoring itemi and itemj , Num () is the number of elements contained in the set, the formula of Devi,j is
Collaborative Filtering (CF) is the most widely used and most successful personalized recommendation algorithm, which is divided into two categories: Userbased collaborative filtering algorithm and Item-based collaborative filtering algorithm
For m users and n items in the recommendation system, the user set is represented by U={u1, ..., um},the set of items is represented by P={p1, ..., pn},the scoring matrix is represented by R, The element ri,j in R is the score of the item pj by the user ui
Summary
The basic method of the algorithm is to use a simple regression expression w=f(v)=v+b, where v is the prediction score of the user u for the project, Ru,j is the average deviation of itemi from the itemj score, denoted as Devi,j, U is defined as a user set, Si,j (U) is a set of users scoring itemi and itemj , Num () is the number of elements contained in the set, the formula of Devi,j is. We can use Devi,j+Ru,j to obtain the predictive score of itemi from user u and get the predicted value after all possible predictions are averaged: P(u)i =. Where: Ri denotes that all users have given item sets whose scores satisfy the criteria (i ≠ j and Si,j are non-empty)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
