In this work, we address the problem of recommending jobs to university students. For this, we explore the utilization of neural item embeddings for the task of content-based recommendation, and we propose to integrate the factors of frequency and recency of interactions with job postings to combine these item embeddings. We evaluate our job recommendation system on a dataset of the Austrian student job portal Studo using prediction accuracy, diversity and an adapted novelty metric. This paper demonstrates that utilizing frequency and recency of interactions with job postings for combining item embeddings results in a robust model with respect to accuracy and diversity, which also provides the best adapted novelty results.
Models that train item embeddings, such as Word2Vec @cite_8 , have been shown to provide good results in many natural language processing tasks. As such, research on improving the recommendation performance by employing word embedding vectors is gaining more momentum. As an example, the authors of @cite_13 used the learned word embeddings trained on Wikipedia for content-based recommender systems. Additionally, in @cite_17 , word embeddings were employed to represent job postings. In order to achieve a high accuracy, a large number of embedding dimensions were required. The authors of @cite_14 utilized item embeddings on top of Collaborative Filtering to tackle the cold-start problem for job recommendations. In addition to that, in the RecSys Challenge of 2016, @cite_16 suggested that time-dependent models can enhance the job recommendation performance, which further motivates the applicability of our BLL-based approach.
@cite_14 @cite_8 @cite_16 @cite_13 @cite_17
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Collaborative Filtering (CF) is widely used in large-scale recommendation engines because of its efficiency, accuracy and scalability. However, in practice, the fact that recommendation engines based on CF require interactions between users and items before making recommendations, make it inappropriate for new items which haven't been exposed to the end users to interact with. This is known as the cold-start problem. In this paper we introduce a novel approach which employs deep learning to tackle this problem in any CF based recommendation engine. One of the most important features of the proposed technique is the fact that it can be applied on top of any existing CF based recommendation engine without changing the CF core. We successfully applied this technique to overcome the item cold-start problem in Careerbuilder's CF based recommendation engine. Our experiments show that the proposed technique is very efficient to resolve the cold-start problem while maintaining high accuracy of the CF recommendations. The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical observations and a hybrid matrix factorization over time re-weighted interactions. Second, we exploit sequence properties in user-items activities and develop a RNN-based recommendation model. Our solution achieved 5th place in the challenge among more than 100 participants. Notably, the strong performance of our RNN approach shows a promising new direction in employing sequence modeling for recommendation systems. In this paper we present a preliminary investigation towards the adoption of Word Embedding techniques in a content-based recommendation scenario. Specifically, we compared the effectiveness of three widespread approaches as Latent Semantic Indexing, Random Indexing and Word2Vec in the task of learning a vector space representation of both items to be recommended as well as user profiles. XING is a leading career-oriented social networking site in Europe, which usually recommend job ads to their customers. One of the widely used methods in Recomender Systems is content-based filtering, which analyzes the description of item characteristics and the user profile illustrating user's preferences. Due to the sparsity of its dataset, i.e. many job postings are rarely interacted with, XING has been using content-based recommender system to promote the quality of the recommendations. Recent word embedding technique learns semantically meaningful representations for words from co-occurrence in sentences, which enables the effective comparison between words. Based on the Word2Vec technique, XING represents job postings by the average embedding over words they contain. This study explores three alternative methods to represent job postings for the task of recommending jobs to users. In the first experiment, we explore whether the use of a subset of words is more effective to represent the job postings. In the second experiment, instead of averaging over word embeddings, we directly learn document embeddings using Paragraph2Vec. And finally, the third experiment uses Word Mover's Distance to estimate the similarity between job postings. Our experiments show that the embeddings that are learned with Paragraph2Vec result in a better estimation of which job postings are similar, but only when high-dimensional settings are used. The Word Mover's Distance algorithm is computationally expensive, therefore we use existing lower-bounds that allowed us to complete a small-scale experiment within the available time. The results indicate that Word Mover's Distance is not as effective as the average over word embeddings and Paragraph2Vec. In the final part of this thesis, we present the Link2Vec, a novel item representation method based on Word2Vec, which learns semantic representations for items based on the context surrounding the hyperlinks that refer to the item, e.g. hyperlinks to the item's Wikipedia page. Our experiments show that the effectiveness of the embeddings learned with Link2Vec improves with the amount of training data. For the evaluation on the MovieLens dataset, we only obtained a limited set of hyperlinks, which resulted in results that approximate a baseline that uses the average over word embeddings.