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… 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 ideal cycle is 14-18).

Context

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.

Content

These datasets contain attributes about products sold on ModCloth Amazon which may be sources of bias in recommendations (in particular, attributes about how the products are marketed).Data includes user/item interactions.

Inspiration

Apply different paradigm, methods and algorithms to recommand right Product to the right Users, during right Time.

*If you find the data useful your upvote is an explicit feedback for future works, Have fun exploring data!*

Global Recommendation Search Engine Market is poised for a significant growth, with market size projected to surge from USD 10.36 Billion in 2024 to USD 30.86 Billion by 2033, showcasing a robust Compound Annual Growth Rate (CAGR) of 12.89% during the forecast period.

The Global Recommendation Search Engine market size to cross USD 30.86 Billion by 2033. [https://edison.valuemarketresearch.com//u

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

Global Machine Learning Recommendation Algorithm Market Report 2023 comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2023-2029. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

Journal recommendations prepared on results from JANE and whichjournal.com based on 4 abstracts from the disciplines dentistry, psychology and aerosol chemistry.

The factsheets with data for each journal should help to decide for the best journal.

The data is provided as spreadsheet (xls) and factsheet (pdf).

The basic statistics of using a PFNF algorithm on Douban datasets.

Dataset

This dataset was created by Gopal sharma

Contents

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.

All the movies are sorted by their degrees in a ascending order (horizontal ordinate). For a given movie , other movies are sorted by their similarities with in a ascending order (vertical ordinate) and the color depth denotes the value of similarity.

Fine-tuned models for the article "Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation" published at NLP4MusA2024. Paper Code

The dataset details 2023 Budget Recommendations, which is the line-item budget document proposed by the Mayor to the City Council for approval. Budgeted expenditures are identified by department, appropriation account, and funding type: Local, Community Development Block Grant Program (CDBG), and other Grants. “Local” funds refer to those line items that are balanced with locally-generated revenue sources, including but not limited to the Corporate Fund, Water Fund, Midway and O’Hare Airport funds, Vehicle Tax Fund, Library Fund and General Obligation Bond funds.

This dataset follows the format of the equivalent datasets from past years except that Appropriation Authority and Appropriation Account have changed from Number to Text in order to accommodate non-numeric values.

For more information about the budget process, visit the Budget Documents page: https://www.chicago.gov/city/en/depts/obm/provdrs/budget.html.

In a 2025 survey, A combined **** percent of all U.S. respondents were comfortable to one degree or another with the use of AI for recipe recommendations. Only *** percent of respondents were very uncomfortable.

This dataset is the supporting data for the paper "Research on Paper Recommendation Based on Academic Knowledge Graph and Biased Random Walk", including pre-processing data from DBLPv14 , main program file and experimental results data.

AI Content Generation Market Outlook

The global AI Content Generation market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 15.3 billion by 2032, growing at a remarkable compound annual growth rate (CAGR) of 22.4% during the forecast period. This significant growth is driven by the increasing adoption of AI technologies across various sectors, the rising demand for personalized and automated content, and advancements in machine learning and natural language processing technologies.

One of the primary growth factors for the AI content generation market is the burgeoning demand for personalized content. Businesses are increasingly leveraging AI to deliver tailored content to their customers, enhancing user engagement and satisfaction. AI algorithms can analyze user data and preferences to generate content that resonates with individual users, thereby improving conversion rates and customer loyalty. This trend is particularly prominent in sectors such as e-commerce, marketing, and media, where personalized content is a crucial competitive differentiator.

Another significant driver is the advancement in natural language processing (NLP) and machine learning (ML) technologies. These advancements have significantly improved the capabilities of AI content generation tools, enabling them to produce high-quality, human-like content. The integration of NLP and ML technologies allows AI systems to understand context, tone, and nuances in language, resulting in more accurate and relevant content generation. This technological progress is expected to continue, further boosting the market growth over the forecast period.

Additionally, the increasing need for cost-efficient content production solutions is fueling the demand for AI content generation tools. Traditional content creation processes can be time-consuming and expensive, involving multiple iterations and requiring skilled professionals. AI-powered content generation tools offer a cost-effective alternative by automating the content creation process, reducing the need for extensive human intervention. This cost efficiency is particularly beneficial for small and medium enterprises (SMEs) with limited budgets, enabling them to compete with larger enterprises.

The evolution of the Content Recommendation Engine has become a pivotal aspect of the AI content generation landscape. These engines utilize sophisticated algorithms to analyze user behavior and preferences, delivering highly personalized content recommendations. By understanding the unique interests of each user, content recommendation engines enhance user engagement and retention, making them indispensable tools for businesses seeking to optimize their content strategies. As AI technologies continue to advance, the capabilities of content recommendation engines are expected to expand, offering even more precise and relevant content suggestions. This progression not only benefits businesses by improving customer satisfaction but also enriches the user experience by providing content that aligns closely with individual interests.

From a regional perspective, North America currently dominates the AI content generation market, owing to the early adoption of advanced technologies and the presence of major AI companies in the region. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation, increasing internet penetration, and growing investments in AI technologies in countries like China, India, and Japan are driving the market growth in this region. Europe also holds a significant share of the market, driven by the strong presence of tech companies and a favorable regulatory environment for AI development.

Component Analysis

The AI content generation market is segmented into software and services. The software segment includes various AI-powered tools and platforms used for content creation, such as text generators, image creators, and video editors. This segment is expected to dominate the market throughout the forecast period, driven by the continuous advancements in AI technologies and the increasing demand for automated content creation solutions. AI software tools are being widely adopted across various industries for their ability to produce high-quality content quickly and eff

apply TODIM to recommend trading model for industry

The user-study evaluates a vocabulary term recommendation service that is based on how other data providers have used RDF classes and properties in the Linked Open Data cloud. The study compares the machine learning technique Learning to Rank (L2R), the classical data mining approach Association Rule mining (AR), and a baseline that does not provide any recommendations. This data collection comprises the raw results of this user-study in SPSS format.