Results 51 to 60 of about 693,638 (193)

Deriving item features relevance from collaborative domain knowledge [PDF]

, 2018
An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based filtering).
Cremonesi, Paolo, Dacrema, Maurizio Ferrari, Gasparin, Alberto   +2 more
core   +1 more source

Content-Based Weak Supervision for Ad-Hoc Re-Ranking

, 2019
One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that ...
Dietz Laura, Hui Kai, Li Bo, Sandhaus Evan, Strohman Trevor   +4 more
core   +1 more source

Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design [PDF]

, 2016
It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history. The lack of historical ratings results in the user and the item cold-start problems.
Anava, Oren, Golan, Shahar, Golbandi, Nadav, Karnin, Zohar, Lempel, Ronny, Rokhlenko, Oleg, Somekh, Oren   +6 more
core   +1 more source

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

IEEE Access
Recommender systems become essential with the presence of the internet and social media. The perceived benefits of the recommender system can make it easier for users to find suitable products and recommend other products, specifically with lots of ...
Ilham Saifudin, Triyanna Widiyaningtyas
doaj   +1 more source

A Hybrid Content-Based and Collaborative Filtering Approach for Recommending Optimal Movie Release Months

Journal of Applied Informatics and Computing
The film industry faces increasingly intense competition, making release timing a critical factor in maximizing box-office performance. However, decisions regarding release months are often driven by producers’ intuition rather than systematic data ...
Ukasyah Muntaha, Johannes Krisjon Silitonga, Elisabeth Claudia Simanjuntak, M. Syamsuddin Wisnubroto, Fajri Farid, Meida Cahyo Untoro   +5 more
doaj   +1 more source

A systematic review and research perspective on recommender systems

Journal of Big Data, 2022
Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet.
Deepjyoti Roy, Mala Dutta
doaj   +1 more source

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

, 2016
In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc.
Basu Chumki, Dai Andrew M, den Oord Aaron Van, Gopalan Prem K, He R., Jozefowicz Rafal, Melville Prem, Mikolov Tomas, Mikolov Tomas, Mnih Andriy, Rendle Steffen, Sutskever Ilya   +11 more
core   +1 more source

A Recommendation System Based on Fuzzy Signature

IEEE Access
In recent years, recommendation systems have become essential tools for managing the overwhelming volume of information users face daily. In the context of Technology Enhanced Learning (TEL), for instance, where learners can be faced with a large variety
Giuseppe D'Aniello, Mario Della Corte, Matteo Gaeta   +2 more
doaj   +1 more source

An evaluation of the role of sentiment in second screen microblog search tasks [PDF]

, 2012
The recent prominence of the real-time web is proving both challenging and disruptive for information retrieval and web data mining research. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms ...
Bermingham, Adam, Smeaton, Alan F.
core  

Movie Recommendation System Using Content Based Filtering

Al-Bāhir, 2023
Sribhashyam Rakesh
doaj   +1 more source