The fintech PicPay offers a wide range of financial services to its 30 million monthly active users, with more than 50 thousand items recommended in the PicPay mobile app. In this scenario, promoting specific items that are strategic to the company can be very challenging. In this work, we present a Switching Hybrid Recommender System that combines two algorithms to effectively promote items without negatively impacting the user's experience. The results of our A/B tests show an uplift of up to 3.2\% when compared to a default recommendation strategy.

Recommender Systems are a subclass of machine learning systems that employ sophisticated information filtering strategies to reduce the search time and suggest the most relevant items to any particular user. Hybrid recommender systems combine multiple recommendation strategies in different ways to benefit from their complementary advantages. Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust. In this paper, we propose a hybrid recommender system, which combines Alternating Least Squares (ALS) based collaborative filtering with deep learning to enhance recommendation performance as well as overcome the limitations associated with the collaborative filtering approach, especially concerning its cold start problem. In essence, we use the outputs from ALS (collaborative filtering) to influence the recommendations from a Deep Neural Network (DNN), which combines characteristic, contextual, structural and sequential information, in a big data processing framework. We have conducted several experiments in testing the efficacy of the proposed hybrid architecture in recommending smartphones to prospective customers and compared its performance with other open-source recommenders. The results have shown that the proposed system has outperformed several existing hybrid recommender systems.

Recommender systems are being employed across an increasingly diverse set of domains that can potentially make a significant social and individual impact. For this reason, considering fairness is a critical step in the design and evaluation of such systems. In this paper, we introduce HyperFair, a general framework for enforcing soft fairness constraints in a hybrid recommender system. HyperFair models integrate variations of fairness metrics as a regularization of a joint inference objective function. We implement our approach using probabilistic soft logic and show that it is particularly well-suited for this task as it is expressive and structural constraints can be added to the system in a concise and interpretable manner. We propose two ways to employ the methods we introduce: first as an extension of a probabilistic soft logic recommender system template; second as a fair retrofitting technique that can be used to improve the fairness of predictions from a black-box model. We empirically validate our approach by implementing multiple HyperFair hybrid recommenders and compare them to a state-of-the-art fair recommender. We also run experiments showing the effectiveness of our methods for the task of retrofitting a black-box model and the trade-off between the amount of fairness enforced and the prediction performance.

Netflix and Prime have such engrossing content that keeps us glued to the screen all the time. There is a section on both of these platforms which displays the recommended content on the basis of the previous content that you have watched. These recommendations seem to be quite relevant to your watch history and the kind of content you would want to engage yourselves with. How this works in the background is by designing certain recommendation systems. Recommendation systems are a set of algorithms which give you recommendations based on your history.

For example, while a recommendation of their behavior such as browsing trails, bookmarks ratings, source based on co-authorship links ranks attendees or created social links. It enables modern recommender systems by its social similarity with the target user, the recommendation to use multiple sources of information about user interests case might require to find attendees who are interested and preferences to deliver better recommendations. This in similar topics while being most likely unknown is most frequently done using parallel hybrid recommendation to the target user (i.e., having the weakest social similarity).

Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation bias exists due to a feedback loop which causes the model to learn to only predict recommendations similar to previous ones. Imbalance in data occurs when systematic societal, historical, or other ambient bias is present in the data. In this paper, we address both biases by proposing a hybrid fairness-aware recommender system. Our model provides efficient and accurate recommendations by incorporating multiple user-user and item-item similarity measures, content, and demographic information, while addressing recommendation biases. We implement our model using a powerful and expressive probabilistic programming language called probabilistic soft logic. We experimentally evaluate our approach on a popular movie recommendation dataset, showing that our proposed model can provide more accurate and fairer recommendations, compared to a state-of-the art fair recommender system.

Then came eBay and Amazon in 1995....... Amazon started as bookstore and eBay as marketplace for sale of goods. Since then, as Digital tsunami flooded, there are tons of websites selling everything on web but these two are still going great because of their product recommendations. We as customers, love that personal touch and feeling special, whether it's being greeted by name when we walk into the store, a shop owner remembering our birthday, helping us personally to bays where products are kept, or being able to customize a website to our needs. It can make us feel like we are single most important customer. But in an online world, there is no Bob or Sandra to guide you through the product you may like.