Item recommendation using user feedback data and item profile

نویسندگان

چکیده

Matrix factorization (MS) is a collaborative filtering (CF) based approach, which widely used for recommendation systems (RS). In this research work, we deal with the content problem users in management system (CMS) on users' feedback data. The CMS applied publishing and pushing curated to employees of company or an organization. Here, have user's data solve problem. We prepare individual user profiles then generate results different categories, including Direct Interaction, Social Share, Reading Statistics, Subsequently, analyze effect categories results. shown that impacts accuracy. best performance achieves if include all types task. also incorporate similarity as regularization term into MF model designing hybrid model. Experimental proposed demonstrates better compared traditional MF-based models.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Context-Aware User-Item Representation Learning for Item Recommendation

Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent representations for users and items in an independent and static manner. That is, a single static feature vector is derived to encode her preference without considering the particular characteristics of each candidate ite...

متن کامل

Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph

Recommender systems can provide users with relevant items based on each user’s preferences. However, in the domain of mobile applications (apps), existing recommender systems merely recommend apps that users have experienced (rated, commented, or downloaded) since this type of information indicates each user’s preference for the apps. Unfortunately, this prunes the apps which are releavnt but a...

متن کامل

Item Recommendation with Evolving User Preferences and Experience

Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user’s experience level and how this is expressed in the user’s writing in a review community. In this paper, we model the joint evo...

متن کامل

Deep Coevolutionary Network: Embedding User and Item Features for Recommendation

Recommender systems often use latent features to explain the behaviors of users and capture the properties of items. As users interact with different items over time, user and item features can influence each other, evolve and co-evolve over time. To accurately capture the fine grained nonlinear coevolution of these features, we propose a recurrent coevolutionary feature embedding process model...

متن کامل

A Boosting Algorithm for Item Recommendation with Implicit Feedback

Many recommendation tasks are formulated as top-N item recommendation problems based on users’ implicit feedback instead of explicit feedback. Here explicit feedback refers to users’ ratings to items while implicit feedback is derived from users’ interactions with items, e.g., number of times a user plays a song. In this paper, we propose a boosting algorithm named AdaBPR (Adaptive Boosting Per...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Nucleation and Atmospheric Aerosols

سال: 2023

ISSN: ['0094-243X', '1551-7616', '1935-0465']

DOI: https://doi.org/10.1063/5.0111349