… syntactic and semantic regularities in text. In this paper, we present a word representation …
similarity among related words in the induced distributed space, our word vectors can be …

… word vectors to obtain the personalized word vectors. The personalized word vectors are
based … framework for training personalized word vectors carrying personalized semantics using …

… personalized query and document representations. Furthermore, these existing models trained
word vectors … -to-end personalization model, which trains personal word vectors with the …

… Therefore, we propose the semantic-expansion approach that integrates semantic information
for spreading expansion and content-based filtering for document recommendation. The …

… interpersonal semantic variations in word meanings, a method for inducing personalized
word … only the impact of semantic variation from the acquired personalized word embeddings. …

… However, it is difficult to classify keywords directly based on semantics. So we need to train
the word vectors first, then cluster according to the word vectors to achieve the effect similar …

… personalize the word embeddings learning, by achieving the learning on the user’s profile.
The word … Here, we describe the process of learning word vectors (see section 1). We use two …

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

… However, some of the interest keywords are not directly linked to the labels if we don’t
understand the semantic meanings. Therefore, in this section we use the word vectors to associate …

… Hence, in this paper a personalized individual semantics (PIS) model is proposed to
personalize individual semantics by means of an interval numerical scale and the 2-tuple linguistic …