Jun 22, 2016 · This paper presents preliminary works on using Word Embedding (word2vec) for query expansion in the context of Personalized Information Retrieval.

This paper presents preliminary works on using Word Embedding (word2vec) for query expansion in the context of Personalized Information Retrieval.

Oct 6, 2016 · The idea is to select words that occur in the same context as the terms of the query. We compare then Word. Embedding learned on the whole ...

Jul 25, 2020 · We design a personalized search model based on personal word embeddings, referred to as PEPS. Specifically, we train personal word embeddings for each user.

Jul 13, 2021 · Embedding based retrieval is based on nearest neighbor search engine. Given a query embedding (q_e), the engine searches for K nearest neighbors in document ...

Toward word embedding for personalized information retrieval. In Neu-IR: The SIGIR 2016 Workshop on Neural In- formation Retrieval, 2016. [8]. Yael Anava ...

Aug 9, 2023 · Embeddings are numerical representations of words or phrases in a continuous vector space. These representations capture semantic relationships ...

Dec 11, 2023 · In this article, we propose a Personalized Query Expansion method designed to solve the issues arising from the use of contextual word embeddings.

... Word Embedding (WEs) is referred to as the process of embedding vocabulary terms as dense, real-valued, and low dimensional vectors. It has proven ...

In this paper, we introduce personalized word embeddings, and examine their value for language modeling. We compare the performance of our proposed prediction ...