A book recommendation system is a type of recommendation system where we have to recommend similar books to the reader based on his interest. The books recommendation system is used by online websites which provide ebooks like google play books, open library, good Read’s, etc.

During the last few decades, with the rise of Youtube, Amazon, Netflix and many other such web services, recommender systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys. In a very general way, recommender systems are algorithms aimed at suggesting relevant items to users (items being movies to watch, text to read, products to buy or anything else depending on industries). Recommender systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors. As a proof of the importance of recommender systems, we can mention that, a few years ago, Netflix organised a challenges (the “Netflix prize”) where the goal was to produce a recommender system that performs better than its own algorithm with a prize of 1 million dollars to win. By applying this simple dataset and related tasks and notebooks , we will evolutionary go through different paradigms of recommender algorithms . For each of them, we will present how they work, describe their theoretical basis and discuss their strengths and weaknesses.

The Sentiment-Based Product Recommendation System stands out as one of the most referenced examples in teaching recommendation systems at leading institutions like IITs (Indian Institutes of Technology), IISc, and top B-schools across India and worldwide. It’s widely used to demonstrate how sentiment from user reviews can elevate both User-Based Recommendation System and Item-Based Recommendation System models—by weaving emotional nuance into data-driven suggestions.

About the Sentiment-Based Product Recommendation System & Review Dataset 🛍️📝

What Makes the Dataset Popular?

• Real-World Scale: It comprises over 30,000+ product reviews spanning 200+ diverse product categories, submitted by more than 20,000+ users.
• Rich Content Mix: The dataset includes:
  • Structured fields like ratings, usernames, sentimental flags, products, brand, manufacturer and more.
  • Unstructured elements such as review titles and text content, ideal for sentiment modeling and explainable recommendations.
• Pedagogically Proven: Frequently adopted in courses and hackathons for teaching how to capture recommendation patterns—especially the differences between User-Based (e.g., "Users similar in taste") and Item-Based (e.g., "Items frequently bought together") recommender systems.

Why It’s a Go-To Teaching Tool

• Simple yet powerful: Combines numerical feedback (ratings) with semantic richness (text reviews), making it ideal for hands-on learning.
• Hybrid Learning Models: Perfect for experimenting with:
  1. User-Based Recommendation System — Recommendations based on users with similar sentiment profiles
  2. Item-Based Recommendation System — Suggestions derived from sentiment-filtered item similarities

Overview of dataset columns and their descriptions

Top Search-Engine Keywords for This Dataset

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Wyze Rule Recommendation Dataset

  Dataset Summary

The Wyze Rule dataset is a new large-scale dataset designed specifically for smart home rule recommendation research. It contains over 1 million rules generated by 300,000 users from Wyze Labs, offering an extensive collection of real-world automation rules tailored to users' unique smart home setups. The goal of the Wyze Rule dataset is to advance research and development of personalized rule recommendation systems for… See the full description on the dataset page: https://huggingface.co/datasets/wyzelabs/RuleRecommendation.

In this study, we built a personalized hybrid course recommendation system (PHCRS) that considers students’ interests, abilities and career development. To meet students’ individual needs, we adopted the five most widely used algorithms, including content-based filtering, popularity-based methods, item-based collaborative filtering, user-based collaborative filtering, and score-based methods, to build a PHCRS. First, we collected course syllabi and labeled each course (e.g., knowledge/skills taught, basic/advanced level). Next, we used course labels and students’ past course selections and grades to train five recommendation models. To evaluate the accuracy of the system, we performed experiments with students in the Department of Electrical and Computer Engineering, which provides 1794 courses for 925 students and utilizes the receiver operating characteristic curve (ROC) and normalized discounted cumulative gain (NDCG) as metrics. The results showed that our proposed system can achieve accuracies of 80% for ROC and 90% for NDCG. We invited 46 participants to test our system and complete a questionnaire. Overall, 60 to 70% of participants were interested in the recommended courses, while the course recommendation lists produced by content-based filtering were in line with 67.40% of students’ actual course preferences. This study also found that students were more interested in courses at the top of the recommendation lists, and more students were autonomously motivated than held extrinsic informational motivation across the five recommendation methods. These findings highlighted that the proposed course recommendation system can help students choose the courses that interest them most.

The data encountered by Session-based Recommendation System(SBRS) is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations.

Dataset

This dataset was created by Kamran Ansari

Released under Database: Open Database, Contents: Database Contents

Contents

the ideal cycle is 14-18).

The size of the Intelligent Recommendation Algorithm to Business market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

The size of the AI-based Recommendation Engine market was valued at USD 3226 million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

The size of the Machine Learning Recommendation Algorithm market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

World Content Recommendation Engine Market is segmented by Application (Media & Entertainment_ E-commerce_ Online Education_ Healthcare_ Financial Services), Type (Collaborative Filtering_ Content-Based Filtering_ Hybrid Systems_ Context-Aware Engines_ Personalized AI), and Geography (North America_LATAM_West Europe_Central & Eastern Europe_Northern Europe_Southern Europe_East Asia_Southeast Asia_South Asia_ Central Asia_ Oceania_ MEA)

The dataset for news recommendation was collected from anonymized behavior logs of News website. The data randomly sampled 1 million users who had at least 5 news clicks during 6 weeks from October 12 to November 22, 2019. To protect user privacy, each user is de-linked from the production system when securely hashed into an anonymized ID. Also collected were the news click behaviors of these users in this period, which are formatted into impression logs. The impression logs have been used in the last week for test, and the logs in the fifth week for training. For samples in the training set, the click behaviors in the first four weeks to construct the news click history for user modeling. Among the training data, the samples on the last day of the fifth week were used as a validation set.

behaviors.tsv The behaviors.tsv file contains the impression logs and users' news click histories. It has 5 columns divided by the tab symbol:

• Impression ID. The ID of an impression.
• User ID. The anonymous ID of a user.
• Time. The impression time with format "MM/DD/YYYY HH:MM:SS AM/PM".
• History. The news click history (ID list of clicked news) of this user before this impression. The clicked news articles are ordered by time.
• Impressions. List of news displayed in this impression and user's click behaviors on them (1 for click and 0 for non-click). The orders of news in a impressions have been shuffled.

news.tsv The docs.tsv contains the detailed information of news articles involved in the behaviors.tsv file. It has 7 columns, which are divided by the tab symbol:

News ID Category SubCategory Title Abstract URL Title Entities (entities contained in the title of this news) Abstract Entities (entites contained in the abstract of this news)

entity_embedding.vec & relation_embedding.vec The entity_embedding.vec and relation_embedding.vec files contain the 100-dimensional embeddings of the entities and relations learned from the subgraph (from WikiData knowledge graph) by TransE method. In both files, the first column is the ID of entity/relation, and the other columns are the embedding vector values. We hope this data can facilitate the research of knowledge-aware news recommendation. An example is shown as follows:

Dataset

This dataset was created by Rohit

Released under Apache 2.0

Contents

This data was collected via a survey on Amazon Mechanical Turk.

The survey describes different driving scenarios including the destination, current time, weather, passenger, etc., and then ask the person whether he will accept the coupon if he is the driver.

Global Product Recommendation Engines Market is segmented by Application (E-Commerce_Retail_Consumer Electronics_Fashion_Travel), Type (Collaborative Filtering_Content-Based Filtering_Hybrid Recommendation Systems_Contextual Product Recommendations_Personalized Email Marketing), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Snapshot of Q-table at some time instance for Q-learning.

The data were collected using a snowball sampling technique on YouTube, starting from specific K-pop album-related keywords and following the chain of recommended videos generated by the platform. Each recommended video was documented, including video titles, subscriber counts, views, and timestamps of the top fifteen recommendations at each level of exploration to analyze patterns in how YouTube’s algorithm circulates K-pop album content.

Personalized news recommendation method that incorporates news popularity information to alleviate the cold-start and diversity problems.

This dataset contains more detailed information on the recommendations and resolutions identified by the Office of the City Auditor in their special request report on gentrification and displacement. See report at: To complete this special request project, we categorized recommendations and resolutions based on the following definitions:

Directly related to displacement or gentrification ("direct"): Clearly and immediately impacts the population experiencing gentrification and displacement (e.g., creation of affordable housing units).

Indirectly related to displacement or gentrification ("indirectly"): An initiative designed to support direct efforts (such as monitoring or increasing funding for staffing). Alternatively, an indirect initiative may be one that is designed to affect the broad population (e.g., overall tax decrease), or the language is so vague or broad that the precise intention is unclear.

Actionable recommendations and resolutions ("actionable"): Recommendations that are specific and implementable as written; recommendations that detail the specific site of an action.

Not actionable recommendations and resolutions ("not actionable"): Things that do not appear to be legal; recommendations that are not implementable as worded or that would require significant additional clarification/research.

Healthcare workers (HCWs) are trusted sources of information for vaccination and their attitude toward vaccination is thus critical. We aimed to synthesize existing literature on healthcare workers’ HPV vaccine confidence and their practices of recommending this vaccine. We conducted a systematic literature review and meta-analysis, with the search conducted last in March 2024. For the inclusion criteria, the studies needed to include healthcare worker practices or behaviors on recommending the HPV vaccination. Seventy-three articles were included. The proportions of HCWs recommending varied considerably by region and gender of the recipient, but there was no statistically significant difference in income level or pre- or post-HPV vaccine introduction into the national vaccination program. The main barriers to recommending HPV vaccination were concerns around safety and efficacy, cost, parental concerns, and systemic barriers. The results illustrate the importance of contextually adapted approaches to improving vaccine acceptance and recommendation.