DSSMFM: Combining user and item feature interactions for recommendation systems
نویسندگان
چکیده
منابع مشابه
User-Weight Model for Item-based Recommendation Systems
Nowadays, item-based Collaborative Filtering (CF) has been widely used as an effective way to help people cope with information overload. It computes the item-item similarities/differentials and then selects the most similar items for prediction. A weakness of current typical itembased CF approaches is that all users have the same weight in computing the item relationships. In order to improve ...
متن کاملCoevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions
Matching users to the right items at the right time is a fundamental task in recommendation systems. As users interact with different items over time, users’ and items’ feature may evolve and co-evolve over time. Traditional models based on static latent features or discretizing time into epochs can become ineffective for capturing the fine-grained temporal dynamics in the user-item interaction...
متن کاملA 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 کاملxDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Combinatorial features are essential for the success of many commercial models. Manually crafting these features usually comes with high cost due to the variety, volume and velocity of raw data in web-scale systems. Factorization based models, which measure interactions in terms of vector product, can learn patterns of combinatorial features automatically and generalize to unseen features as we...
متن کاملDeep 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 کاملذخیره 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: MATEC Web of Conferences
سال: 2020
ISSN: 2261-236X
DOI: 10.1051/matecconf/202030903010