Abstract
Preferring accuracy over computation time or vice versa is very challenging in the context of recommendation systems, which encourages many researchers to opt for hybrid recommendation systems. Currently, researchers are trying hard to produce correct and accurate recommendations by suggesting the use of ontology, but the lack of techniques renders to take its full advantage. One of the major issues in recommender systems bothering many researchers is pure new user cold-start problem which arises due to the absence of information in the system about the new user. Linked Open Data (LOD) initiative sets standards for interoperability among cross domains and has gathered enormous amount of data over the past years, which provides various ways by which recommender system’s performance can be improved by enriching user’s profile with relevant features. This research work focuses on solving pure new user cold-start problem by building user’s profile based on LOD, collaborative features, and social network-based features. Here, a new approach is devised to compute item similarity based on ontology, thus predicting the rating of nonrated item. A modified method to calculate user’s similarity based on collaborative features to deal with other issues such as accuracy and computation time is also proposed. The empirical results and comparative analysis of the proposed hybrid recommendation system dictate its better performance specifically for providing solution to pure new user cold-start problem.
Highlights
Fetching desired information from websites or apps containing enormous data such as items, videos, pictures, and text is a very challenging and time-consuming task
(ix) Once we find the “User cluster” to which new user belongs to, system analyzes the rating provided to each “Item cluster” by only those users who are present in this predicted “User cluster.”
Similarity between the users is calculated based on the features, i.e., by analyzing the social network features, collaborative features from Dense User-Item Matrix (DUIM), and the features extracted from the information coming from the Linked Open Data (LOD) cloud
Summary
Fetching desired information from websites or apps containing enormous data such as items, videos, pictures, and text is a very challenging and time-consuming task. New user cold-start problem refers to the lack of information about user’s interest or very less ratings provided by this user for any particular item in the system. With the increasing e-commerce platforms, huge numbers of new users signing every day or less-active users in almost every application create a serious issue for the recommendation systems [2] Another major issue is new item coldstart problem which refers to the newly added item in any particular system which has very less or no rating provided by the user, so in this scenario analyzing the item and referring it to the user can be a tedious task [3]. Is works focuses on resolving the cold-start problem by finding the similarity between users based on their social, collaborative, and Linked Open Data features.
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