Classifications,evaluation metrics,datasets,and domains in recommendation services: A survey

2.1 Content-based recommendation systems

Content-based Recommender Systems (RS) propose items to users by leveraging their data directly or indirectly. The concept of content-based RS is illustrated in Fig. 1. These systems analyze previously rated documents or descriptions to construct a comprehensive user profile that encapsulates their historical rating patterns [43, 63]. This user profile becomes the cornerstone of the recommendation process, aiding in identifying items that align with the user’s preferences. The application of content-based RS extends across diverse domains, prominently featuring in Information Retrieval systems (IR) and the realm of artificial intelligence [53].

The foundational mechanism of content-based strategies centers around key concepts such as term frequency (TF), inverse document frequency (IDF), and the cosine similarity matrix. These analytical tools play a pivotal role in evaluating the significance of various types of content, including documents, articles, news items, movies, and more [69]. In this approach, a user’s preferred or purchased item is the catalyst. The system meticulously dissects the selected item’s contents, categories, and attributes to discern its unique traits. Subsequently, armed with this insightful analysis, the system identifies other items within its database that share similar characteristics. The recommendation is then based on these similarity scores, presenting the user with suggestions that closely align with their preferences and past choices [1].

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The comparative landscape of research in content-based approaches is encapsulated in Table 1. This table offers a comprehensive overview, highlighting the distinctions and nuances across various studies in content-based recommender systems. The comparative analysis aims to shed light on the diverse methodologies employed, providing researchers and practitioners with valuable insights into the evolution and efficacy of content-based recommendation strategies.

Collaborative Filtering (CF) is a pivotal and widely adopted approach in various industries, playing a crucial role in platforms such as Amazon, Flipkart, and Netflix to elevate product promotion and boost sales [18]. Figure 2 shows the principle of the process of CF-based RS. The roots of CF can be traced back to its initial application by developers at Xerox in document retrieval systems, with Google’s Page Rank Algorithm serving as an illustrative example of CF applied to document retrieval. Within Recommender Systems (RS), CF techniques pivot on the rich history of user interactions and items, relying on a comprehensive record of these interactions as input. Predictions for users are crafted based on their past item ratings [2].

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Two distinct categories of recommendation algorithms emerge within CF: memory-based and model-based methods [50]. Memory-based algorithms, characterized by their simplicity and lack of model-building requirements, depend on the correlations between items (item-based) or users (user-based). These algorithms effectively furnish recommendations using distance measurements such as nearest neighbor (k-nearest neighbor) [44]. In contrast, model-based algorithms leverage prediction models based on the user’s preference matrix (rating matrix) to generate recommendations [39]. Techniques like matrix factorization, Bayesian networks, genetic algorithms, clustering, singular value decomposition (SVD), and deep learning fall under model-based approaches.

Despite its efficacy, CF grapples with challenges like data sparsity, the cold-start problem, and scalability. To address scalability, Zhao et al. [71] devised a user-based CF technique tailored for the MapReduce framework, implemented on the widely-used Hadoop cloud computing platform. This innovative design aims to surmount scalability issues and enhance computational speed.

With the expanding use of Recommender Systems on the internet, a need arises to evaluate CF methods and establish measures to assess recommendation quality and user trust. Bobadilla et al. [6] introduced a framework prioritizing the evaluation of user recommendation novelty and trust in neighbors. Their work presents an equation formalizing and unifying the CF process and its evaluation, constructing the framework using four graphs: quality of predictions, recommendations, novelty, and trust. Addressing the data sparsity challenge inherent in CF algorithms, active learning algorithms emerge as effective solutions. Bobadilla et al. [24] introduced Bayesian Nonnegative Matrix Factorization (BNMF) techniques and an original clustering algorithm, enhancing CF-based recommendation system performance. This technique demonstrates high prediction accuracy and swift execution times, offering flexibility and accuracy to enhance overall system performance. As evidenced by these advancements, the constant evolution and innovation in CF methodologies underscore the dynamic nature of collaborative filtering within Recommender Systems.

Numerous challenges plague RS, such as the cold-start issues, where providing recommendations for new items is challenging due to the need for rating history. Other problems, the data sparsity and the scalability, also afflict RS, resulting in subpar quality and unsuitable suggestions. While the content-based aims to recommend cold-start items, their recommendations’ accuracy is generally lower compared to CF approaches. Conversely, CF-based systems provide better recommendations but must improve in addressing the cold-start problem and data sparsity problems. In the end, each of these techniques boasts weaknesses and strengths.

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A strategic hybrid approach has emerged in response to the intricacies inherent in recommendation systems (RS), amalgamating diverse forms of information derived from individual RS techniques. This integration alleviates the complexities of singular methodologies, ultimately refining recommendation outcomes [19, 60]. A prime illustration of the efficacy of hybrid RS can be observed in Netflix’s operations. The streaming platform adeptly tailors its suggestions by analyzing users’ viewing and browsing behaviors through collaborative filtering (CF). Additionally, it refines recommendations by proposing films that align with highly-rated selections made by the user [66]. This hybrid model enables Netflix to offer personalized and diverse content suggestions, enhancing the overall user experience. Robin Burke’s seminal survey on hybrid recommendation techniques identifies seven distinct categories, shedding light on the versatile strategies employed in hybrid RS [8]:

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Weighted: This technique combines scores or points from different recommendation methodologies to produce a unified and comprehensive recommendation. A balanced and optimized recommendation is derived by assigning weights based on the relevance or effectiveness of each approach.

Adopting hybrid RS, exemplified by platforms like Netflix,1

Table 2 compares authors’ efforts across five papers, utilizing Hybrid-based RS on multiple datasets. The lower MAE values correspond to the better model regarding the accuracy of the recommendations. In this table, it becomes evident that the hybrid RS proposed in [62] showcases the lowest MAE value and most minimal processing time. In contrast, the experiment deployed in [17] used the FilmTrust dataset, yielding the highest MAE value, which means lower accuracy for the RS. Further, when comparing two methods introduced in [32] for a movie recommendation system, the alternative hybrid model outperforms the switching hybrid recommendation model in terms of accuracy. Among the experiments detailed in [30], the one conducted on the Last.fm dataset records the lowest MAE value. However, it’s noteworthy that this MAE value, while minimal, still exceeds the accuracy achieved by the model proposed in [10].

This section describes various recommendation methods beyond conventional collaborative filtering and content-based approaches. Each method showcased here offers unique insights and addresses specific challenges in tailoring recommendations to user preferences. We explore various methodologies, from knowledge-based recommendation systems that leverage domain expertise to cross-domain systems broadening their recommendation scope and context-aware systems that adapt to users’ changing preferences to demographic filtering that taps into user profiles.

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Knowledge-based Recommendation Systems represent a sophisticated approach to tailoring recommendations using user and item information. These systems go beyond traditional collaborative filtering and content-based methods, focusing on assessing the similarity between user profiles and item data to identify the most suitable items for users. The recommendation component of Knowledge-based Recommendation Systems is illustrated in Fig. 4. Unlike other methods, knowledge-based systems overcome cold-start issues by relying on domain knowledge rather than solely dependent on user ratings [2]. This reliance on domain knowledge becomes particularly beneficial in specialized contexts such as e-learning, where personalized recommendations leveraging ontology for knowledge representation are recommended [7]. Educators, too, can harness the potential of knowledge-based recommendation systems for teaching and learning activities facilitated by ontological modeling [38]. However, a notable drawback of these systems is their reliance on knowledge engineering skills, which can be challenging to acquire and implement effectively [8].

These diverse recommendation approaches illustrate the flexibility and applicability of recommendation systems in various domains. Each approach comes with its strengths and challenges, and the choice of a specific method or a combination of methods depends on the characteristics of the user base, the available data, and the specific goals of the recommendation system.

6.1 Cold start problem

The Cold Start Problem represents a formidable challenge in collaborative filtering (CF) systems, particularly when confronted with the introduction of new users or novel items [70]. This predicament manifests across three distinct scenarios, each presenting unique challenges:

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New User Access: When a new user enters the system, CF models encounter a scarcity of historical interaction data to generate accurate recommendations. This lack of user-specific information hampers the system’s ability to understand and predict the preferences of the new user.

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New Item Introduction: The Cold Start Problem arises when a platform introduces new items into its catalog. Since these items lack a history of user interactions, CF algorithms struggle to assess their relevance to users, leading to potential inaccuracies in recommendations.

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Creation of a New Community Group: Establishing a new community group within a system presents another instance of the Cold Start Problem. When users form or join a new group, the collaborative filtering approach encounters challenges in predicting preferences and building accurate recommendations for the group.

A viable solution to these challenges involves adopting a hybrid approach combining content-based and CF techniques. This hybrid model leverages additional information, such as product descriptions, features, and user profiles, to enhance recommendation quality in the face of the Cold Start Problem.

For example, consider a scenario where a new user signs up for a movie streaming service. Traditional CF models might struggle initially to provide accurate movie recommendations due to the absence of the user’s viewing history. However, by integrating content-based techniques, the system can analyze the features and descriptions of movies to understand the genre preferences or themes that might align with the user’s interests. This hybrid approach ensures the system can generate relevant and personalized recommendations for the new user without historical interaction data.

Similarly, when introducing new items to an e-commerce platform, the hybrid model can consider product descriptions, attributes, and user profiles to make informed suggestions. By combining the collaborative insights derived from user-item interactions with the inherent characteristics of items, the system overcomes the limitations imposed by the Cold Start Problem. It delivers more accurate recommendations for both new and existing items.

In essence, the hybrid approach acts as a strategic alliance, merging the strengths of content-based and CF techniques to create a more resilient and adaptable recommendation system. By addressing the intricacies of the Cold Start Problem through this comprehensive strategy, recommendation systems can offer improved accuracy and relevance, ensuring a seamless user experience even in scenarios characterized by new users, new items, or the creation of new community groups.

6.2 Data sparsity

The issue of data sparsity in recommendation systems arises from sparse user-item interaction matrices, where users have not rated all available objects. This sparsity can impede efficient computational learning and compromise the accuracy of recommendations. Mitigating data sparsity is crucial for enhancing the performance of recommendation systems. One effective strategy involves applying dimensionality reduction techniques, as highlighted in the study [2].

Dimensionality reduction techniques play a pivotal role in alleviating data sparsity by addressing the inherent challenges posed by incomplete user ratings [46]. By reducing unnecessary dimensions in the user-item interaction matrix, these techniques help distill meaningful patterns and relationships from the data. Essentially, they trim down the complexity of the matrix by eliminating irrelevant information or noise associated with products that lack substantial user ratings.

For example, consider a movie recommendation system where users have not rated every available film. The user-item interaction matrix for this system would be sparse, making it challenging to accurately predict user preferences. By applying dimensionality reduction techniques, the system can identify and retain the most relevant features, such as genres, themes, or user characteristics, while discarding less informative dimensions. This streamlined representation allows recommendation algorithms to work with a more condensed and informative user rating matrix, improving computational efficiency and recommendation accuracy.

In the broader context of e-commerce or content platforms, where users may not interact with or rate all available products, dimensionality reduction becomes a valuable tool. By eliminating redundant or less informative product features, these techniques enhance the system’s ability to discern meaningful patterns from the sparse data, ultimately contributing to more accurate and personalized recommendations.

In recap, tackling data sparsity through dimensionality reduction techniques is critical in optimizing recommendation systems. By refining the user-item interaction matrix and focusing on the most relevant dimensions, these techniques enhance computational efficiency and contribute to the overall effectiveness of recommendation algorithms, ensuring users receive personalized and meaningful suggestions even in sparse data scenarios.

6.3 Scalability

The scalability challenge in recommender systems becomes particularly pronounced when dealing with extensive user bases and vast catalogs of products, where the computational intensity of generating and evaluating recommendations can be overwhelming [35]. A notable example of a platform addressing scalability challenges is Amazon, which employs a strategic approach to enhance the efficiency and effectiveness of its recommendation system.

To tackle the scalability issue, Amazon utilizes a combination of collaborative filtering (CF) and a topic diversification algorithm. Collaborative filtering, a widely adopted recommendation technique, analyzes user-item interactions to identify patterns and similarities, providing personalized recommendations based on user preferences. However, as the user and product databases grow exponentially, the computational demands of CF increase significantly.

In response, Amazon incorporates a topic diversification algorithm into its recommendation system. This algorithm introduces complexity by diversifying the recommended items based on thematic or topical relevance. Doing so ensures that recommendations are personalized and exhibit a certain degree of variety, catering to users’ diverse interests.

For instance, consider a user who frequently purchases books in the science fiction genre on Amazon. While collaborative filtering might recommend additional science fiction titles based on user preferences, the topic diversification algorithm ensures that the recommendations include books from related genres, providing a more well-rounded and diverse set of suggestions.

Integrating topic diversification alongside CF is a strategic solution to the scalability challenge. It allows Amazon to efficiently handle the computational complexities associated with millions of users and products while enhancing the quality and diversity of recommendations. This approach mitigates scalability issues and contributes to a more engaging and satisfying user experience by presenting various relevant suggestions.

6.4 Grey sheep problem

The Grey Sheep Problem poses a unique challenge in collaborative filtering Recommender Systems (CF RS), particularly when anomalous users or user groups defy neat categorization, falling outside the typical alignment with specific categories or exhibiting characteristics that match multiple types [72]. This anomaly complicates generating accurate recommendations, as traditional collaborative filtering methods may struggle to discern patterns or similarities for these users. Addressing the Grey Sheep Problem requires innovative solutions to ensure the accuracy and effectiveness of the recommendation system.

One notable strategy for mitigating the Grey Sheep Problem involves offline clustering methods, such as k-means. These methods help distinguish grey sheep users – those who deviate from conventional user patterns – from regular users. By clustering users based on their behavior, preferences, or interactions, the system can identify and treat anomalous patterns separately, ensuring more accurate and tailored recommendations for users in the grey sheep category.

Additionally, techniques like histogram intersection play a crucial role in refining the user-user similarity distribution, contributing to the resolution of the Grey Sheep Problem. Histogram intersection involves comparing the distribution of preferences or behaviors between users, providing a more nuanced understanding of their similarities. This method enables the system to capture subtle patterns and relationships that might be overlooked by traditional collaborative filtering approaches, enhancing the accuracy of recommendations, especially for users with diverse or anomalous preferences.

For instance, imagine a collaborative filtering system for a streaming platform. Traditional collaborative filtering might struggle with users who have eclectic tastes, enjoying content spanning multiple genres or categories. The Grey Sheep Problem arises when these users do not neatly fit into predefined categories. By applying offline clustering methods like k -means and incorporating histogram intersection techniques, the recommendation system can better capture the nuances of these eclectic users, providing recommendations that align with their unique preferences.