Joint Text Embedding for Personalized Content-based Recommendation

نویسندگان

  • Ting Chen
  • Liangjie Hong
  • Yue Shi
  • Yizhou Sun
چکیده

Learning a good representation of text is key to many recommendation applications. Examples include news recommendation where texts to be recommended are constantly published everyday. However, most existing recommendation techniques, such as matrix factorization based methods, mainly rely on interaction histories to learn representations of items. While latent factors of items can be learned e‚ectively from user interaction data, in many cases, such data is not available, especially for newly emerged items. In this work, we aim to address the problem of personalized recommendation for completely new items with text information available. We cast the problem as a personalized text ranking problem and propose a general framework that combines text embedding with personalized recommendation. Users and textual content are embedded into latent feature space. Œe text embedding function can be learned end-to-end by predicting user interactions with items. To alleviate sparsity in interaction data, and leverage large amount of text data with liŠle or no user interactions, we further propose a joint text embedding model that incorporates unsupervised text embedding with a combination module. Experimental results show that our model can signi€cantly improve the e‚ectiveness of recommendation systems on real-world datasets.

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

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Joint Semantic Vector Representation Model for Text Clustering and Classification

Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...

متن کامل

Content2vec: Specializing Joint Representations of Product Images and Text for the Task of Product Recommendation

We propose a unified product embedded representation that is optimized for the task of retrieval-based product recommendation. We generate this representation using Content2Vec, a new deep architecture that merges product content information such as text and image, and we analyze its performance on hard recommendation setups such as cold-start and cross-category recommendations. In the case of ...

متن کامل

Exploiting Rich Contents for Personalized Video Recommendation

Video recommendation has become an essential way of helping people explore the video world and discover the ones that may be of interest to them. However, mainstream collaborative filtering techniques usually suffer from limited performance due to the sparsity of user-video interactions, and hence are ineffective for new video recommendation. Although some recent recommender models such as CTR ...

متن کامل

LDA-based Personalized Document Recommendation

Accompanying with the rapid growth of Internet, people around the world can easily distribute, browse, and share as much information as possible through the Internet. The enormous amount of information, however, causes the information overload problem that is beyond users’ limited information processing ability. Therefore, recommender systems arise to help users to look for useful information w...

متن کامل

Intelligent Approach for Attracting Churning Customers in Banking Industry Based on Collaborative Filtering

During the last years, increased competition among banks has caused many developments in banking experiences and technology, while leading to even more churning customers due to their desire of having the best services. Therefore, it is an extremely significant issue for the banks to identify churning customers and attract them to the banking system again. In order to tackle this issue, this pa...

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

عنوان ژورنال:
  • CoRR

دوره abs/1706.01084  شماره 

صفحات  -

تاریخ انتشار 2017