Deep Learning-Based Analysis of E-Commerce Enterprises

Collaborative filtering (CF), the most successful and widely used technique, recommends items based on the preferences of similar users. The main potentials of CF are its cross-genre recommendation ability, and that it is completely independent of representation of the items being recommended. However, CF suffers from sparsity and cold start problems. On the other hand, a highly effective variant of content-based filtering (CBF), reclusive methods (RMs) based on the preference of the single individual for whom recommendations to be made, provides a methodology that considers uncertainty and the multivalued nature of item features as well as user preferences in a content-based framework using fuzzy logic approaches. The adoption of RM paradigm has several advantages when compared to CF such as sparsity and new item problem, but it suffers from overspecialization and limited content analysis. In view of the complementary nature of CF and RM, we develop a hybrid recommender system (RS) that helps in alleviating aforementioned problems in each approach. First, we propose fuzzy naïve Bayesian classifier based CF (FNB-CF) and RM (FNB-RM) for handling correlation-based similarity problems. To overcome individual weaknesses of FNB-CF and FNB-RM, we develop a hybrid RS, FNB-CF-RM. Effectiveness of our proposed hybrid RS is demonstrated through experimental results using the MovieLens and IMDb data sets.

When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user’s purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.

This paper discusses the design and evaluation of a Neural Collaborative Filtering (NCF) model for movie recommendations using the MovieLens dataset. It addresses the limitations of traditional recommendation systems, such as content-based filtering and collaborative filtering, which struggle with data sparsity and the cold start problem. By incorporating deep learning, the NCF model enhances the accuracy and personalization of recommendations by learning the latent features of users and items and capturing complex interactions.

In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well‐known CF recommenders suffer from data sparsity, mainly in cold‐start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state‐of‐the‐art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low‐dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state‐of‐the‐art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non‐linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF‐based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.

The 'Cold Start' problem, characterized by insufficient transaction history leading to inefficient personalization, represents one of the frequent challenges encountered in e-commerce systems. This issue, along with data sparsity resulting from limited product interactions, further complicates the reliability of conventional recommendation engines. The objective of this research is to design a novel hybridized recommendation system that enhances both security and suggestion accuracy by dynamically adapting to user interactions in digital environments. By leveraging contextual information and sequential user behavior patterns, the proposed method addresses gaps left by traditional recommender systems. A research methodology is employed that integrates context awareness and sequential pattern mining, combining conventional and advanced recommendation techniques within a unified framework. This hybrid strategy tackles challenges posed by Cold Start scenarios while optimizing the use of scarce user interaction data. Preliminary findings indicate that the hybrid approach significantly improves recommendation performance, particularly in settings with limited historical data. By utilizing contextual and sequential information, the proposed system outperforms existing methods in terms of accuracy and personalization. In conclusion, the study demonstrates the effectiveness of hybrid recommendation systems in addressing fundamental e-commerce challenges such as data sparsity and the Cold Start problem. The adaptability of the approach enables accurate predictions without requiring extensive user history. Future research will focus on the scalability of this strategy across diverse e-commerce platforms to deliver more individualized user experiences.

This paper presents a comprehensive review of movie recommendation systems and their applications in the entertainment industry. The paper highlights the different techniques used in movie recommendation systems such as collaborative filtering, content-based filtering, and hybrid filtering. The strengths and weaknesses of each technique are discussed, along with their respective algorithms. The paper also explores the challenges faced by movie recommendation systems such as data sparsity, cold start problem, and user bias. Furthermore, the paper examines the current state-of-the-art movie recommendation systems, their performance evaluation metrics, and their implementation in real world scenarios. The practical application of these systems by popular online streaming platforms is also discussed. The study concludes that movie recommendation systems are essential for enhancing user experience and engagement in the entertainment industry, and future research in this field is crucial to improving the accuracy and effectiveness of these systems. Key Words: Recommendation System, PyCharm, Jupiter Notebook, Artificial Intelligence & Machine Learning.

Context-aware recommender systems (CARSs) have emerged from traditional recommender systems (RSs) that provide several different opportunities in the area of personalized recommendations for online users. CARSs promote incorporation of additional contextual information such as time, day, season, user’s personality along with users and items related information into recommendation process that makes market based e-commerce sites more attractive to users. Content-based filtering (CBF) and collaborative filtering (CF) are two well-known and most implemented recommendation techniques that offer various hybridization approaches for producing quality recommendations. Moreover, contextual pre-filtering, contextual post-filtering and contextual modeling are some paradigms through which CARSs take advantages of user’s contextual preferences in recommendation process. In this paper, we introduce a decision tree based CARS framework that exploits the benefits of both CBF and CF techniques using contextual pre-filtering paradigm. We apply ID3 algorithm for learning a user model to exploit the user’s contextual preferences and utilizing rules extracted from decision tree to neighborhood formation. Experimental results using two real-world benchmark datasets clearly validate the effectiveness of our proposed scheme in comparison to traditional scheme.

The cold-start problem faced by recommender systems is a serious problem, mainly because of the lack of historical data for new users or items. Traditional recommendation techniques, such as collaborative filtering and content-based filtering, are prone to fail in making good recommendations under such conditions. This paper explores the use of reinforcement learning (RL) as a remedy for cold-start problems based on active learning methods and multi-armed bandit models. We propose a novel RL-based approach that learns user preferences incrementally from interaction and improves recommendations in an exploration-exploitation setting. The setting shows improved performance in personalization and user engagement compared to baseline methods. This paper also provides the first-order implications of the cold-start problem in the real world and assesses the challenges faced by industries impacted by this problem. Keywords - Recommender systems, cold-start problem, reinforcement learning, Markov decision processes, deep reinforcement learning, user modelling

Recommender System is an efficient tool that consider an individual's opinion to identify their content more appropriately and selectively. This system has been applied to various domain, but in the field of research area, service based recommendation system plays a major role. In recent years, the growth rate of online hotel searching has been increased much faster and makes this online hotel searching a very difficult task due to the abundant amount of online information. Reviews written by the travelers replaces the word-of-mouth but then to searching becomes the time consuming task based on user preference. Reviews crawled from the travelers visiting sites are a common and valuable source of information for recommendation of a hotel, yet little attention has been paid as, how to present the reviews of a reviewers in an understandable format. The purpose of this project is to recommend the travelers the name of the hotels based on their preferences, by analyzing the other travelers reviews together with the rating value to improve the prediction accuracy. Users do not rate adequate hotels to enable collaborating filtering based recommendation, which can lead to an issue called as cold start problem. To solve this issue context-aware hybrid approach is used where collaborative filtering technique aggregates with the sentimental analysis, to provide personalized hotel recommendation. Here aspect based sentiment analysis is performed where weights of each aspect is calculated to find its orientation score. Also we use semi-supervised clustering algorithm to group the aspects of similar meaning. Lexicon based approach is used to identify the sentiment towards the hotel aspects based on the defined context. Thus, context-based approach increases the recommendation results.

This study conducts a rigorous investigation into the financial and economic implications of tech sector layoffs on consumer spending patterns in the San Francisco Bay Area. Utilizing a unique dataset comprising daily and weekly data from March 13, 2020, to February 19, 2023, the analysis employs local projection methods to explore the dynamic relationship between layoffs and consumer spending. The daily data analysis reveals a statistically significant negative correlation between tech layoffs and consumer spending, with an estimated decrease of 0.002 percentage points in spending per additional layoff at day 8 (p-value of 1.71e-12). These findings align with the theoretical underpinnings of Mueller (1966) and Reich (2001), which emphasize the impact of unemployment on consumer spending. Conversely, the weekly data analysis yields mixed results, with significant fluctuations challenging the derivation of concrete insights. To alleviate the detrimental effects of job losses, the study recommends implementing comprehensive severance packages, outplacement support services, and fostering a culture of continuous learning within the workforce. By providing a robust analysis of the intricate relationship between tech sector layoffs and consumer spending patterns, this research offers valuable insights for future studies and policy development in the context of workforce displacement.

As we are familiar with the increasing number and demand for online videos on the internet has led to difficulty in the extraction of required data. The recommendation system is used to filter or to collect information according to the user's preference. Nowadays, the use of recommendation systems has become important so that we can find more relevant data from huge sources. Based on the attributes such as genres, we can recommend important videos to different users. There are different video recommendation problems such as cold start, data sparsity, etc. Here in this paper, we are solving the data sparsity problem which is considered as the biggest problem as it can give poor results. There are different recommendation techniques that are used to solve various recommendation problems such as content-based filtering, real-time recommendation system, hybrid recommender system, single network-based recommendation system, collaborative filtering. We are using a single-network recommendation system to enhance the quality of recommendation.

Cooking your own food at home is a good step toward reducing fast food consumption. Fast food increases the risk of dangerous diseases. The diversity of recipe information available on the internet makes it difficult to choose recipes that match user preferences. Mobile technology can help with this by recommending recipes that better suit users' eating habits. This makes the transition to a healthier diet easier. Therefore, in this study, a recommendation system was developed that can recommend recipes based on the preferences of Android users. Two main recommendation methods are used in this study: content-based filtering and collaborative filtering. Using cosine similarity, a content-based recommendation system identifies the proximity between a recipe for food and its related context. The history of user comments on recipes serves as implicit feedback for the collaborative recommendation algorithm. This eliminates the need for explicit evaluations, such as ratings. This recommendation system generates recommendations in the form of the top ten food recipes with an evaluation matrix, referred to as NDCG@k and Hit-Ratio@k. The tests revealed that a content-based filtering technique may produce helpful recommendations, with the highest similarity score of 0.41 for the entry "chocolate cake that you can easily make at home." Meanwhile, in the collaborative filtering method using the Neural Collaborative Filtering (NCF) approach, the system shows consistent performance improvements, with the MAP@10 value increasing from 0.705 to 0.767 and the NDCG@10 from 0.78 to 0.83 after 10 training epochs. Keywords: Recommendation systems; content-based filtering; neural collaborative filtering; cosine similarity; implicit feedback

Now that we have so much information available, recommender systems are becoming more and more important in many different areas. Traditional methods are very popular because they work well, but they have problems with the cold-start problem and data sparsity. Large language models (LLMs) are a new breakthrough for recommender systems because they can understand and generate text in natural language. This study suggests a better recommendation method that uses both collaborative filtering and LLMs. This method combines the strengths of both methods, using collaborative filtering to model user preferences and LLMs to improve understanding of user and item information. This improves the accuracy and variety of recommendations. In this paper, we start by explaining the basic ideas behind collaborative filtering and LLMs. Then, we design a recommendation system that combines the two and test how well it works. The results show that the hybrid model based on collaborative filtering and LLM significantly improves accuracy, recall and user satisfaction, demonstrating its potential in complex recommendation scenarios.

Collaborative filtering (CF) is one of the most popular recommender system technologies. It tries to identify users that have relevant interests and preferences by calculating similarities among user profiles. The idea behind this method is that, it may be of benefit to one's search for information to consult the preferences of other users who share the same or relevant interests and whose opinion can be trusted. However, the applicability of CF is limited due to the sparsity and cold-start problems. The sparsity problem occurs when available data are insufficient for identifying similar users (neighbors) and it is a major issue that limits the quality of recommendations and the applicability of CF in general. Additionally, the cold-start problem occurs when dealing with new users and new or updated items in web environments. Therefore, we propose an efficient iterative prediction technique to convert user-item sparse matrix to dense one and overcome the cold-start problem. Our experiments with MovieLens and book-crossing data sets indicate substantial and consistent improvements in recommendations accuracy compared with item-based collaborative filtering, singular value decomposition (SVD)-based collaborative filtering and semi explicit rating collaborative filtering.

This paper presents a novel hybrid recommendation system approach that combines collaborative filtering, content-based filtering, and deep learning techniques to improve recommendation accuracy and overcome common challenges in e-commerce platforms. Our proposed model addresses key limitations such as the cold-start problem, data sparsity, and overspecialization by leveraging the complementary strengths of multiple recommendation strategies. Implementation using Python demonstrates significant performance improvements across various evaluation metrics compared to standalone methods, providing a practical framework for e-commerce recommendation systems.