This is a step-by-step beginner's guide for conducting experiments in Recommender System research, which often involves Natural Language Processing (NLP) techniques. It also serves as a guide for NLP research, particularly focusing on Sentiment Analysis. Recommender System research may include analyzing user reviews text using Sentiment Analysis and Machine Learning methods.

Before embarking on research related to Recommender Systems or NLP experiments, it’s crucial to have a clear understanding of the key points outlined below. Grasping these fundamentals will simplify the process of reviewing other research papers during your literature review.

Note:In any research, it's essential to establish a baseline for comparison. You then propose a new or modified framework, assuming it will outperform the baseline. After conducting the experiment, you evaluate both the baseline and your proposed framework. The evaluation results will determine whether your proposed framework is superior to the baseline.

I will outline the key steps and the techniques used at each stage. However, detailed descriptions of each technique and term will not be included.

Data Preparation

  • A dataset can be created either custom by applications or individuals, or it may be sourced from freely available datasets online.
  • Pre-processing the dataset (such as converting all text to lowercase, removing punctuation, etc.) is essential before using it in experiments.

Training a Classifier & Sentiment Analysis

A classifier can be trained using a set of positive and negative reviews or texts. Once trained, the classifier can categorize documents or texts into various categories.

There are several types of classifiers available, including:

  • Naive Bayes classifier
  • Logistic Regression classifier
  • Decision Trees classifier
  • Support Vector Machine (SVM) classifier
  • Maximum Entropy classifier

Training a classifier requires a set of features.

Various methods can be used for feature selection, including:

  • Bag of Words
  • Term Frequency and Inverse Document Frequency (TF-IDF)
  • Word to Vector
  • Latent Semantic Indexing (LSI)
  • Latent Dirichlet Allocation (LDA)
  • Dependency Structure Tree Analysis

After training the classifier with a set of positive and negative text reviews, you can use it to classify new review texts. The trained classifier should be capable of categorizing these reviews as positive or negative.

The classifier can assign labels such as positive, negative, or neutral to individual words. Next, you need to perform sentiment analysis on the entire review text.

Sentiment Analysis on texts can be performed in various ways, like:

  • Document level analysis
  • Sentence level analysis
  • Entity and Aspect level analysis

To assess the overall sentiment (positive or negative) of a review, "Sentence level analysis" can be used. This approach involves performing sentiment analysis on each individual sentence within the review.

If the goal is to evaluate various entities and aspects within the review, "Entity and Aspect level analysis" would be more appropriate.

Recommendation Techniques

In research on recommender systems, various techniques are employed to suggest a set number of items to users. Typically, these recommendation techniques fall into three main categories:

  • Content-Based Filtering (CBF) technique
  • Collaborative Filtering (CF) technique
    • User-based Collaborative Filtering
    • Item-based Collaborative Filtering
  • Hybrid technique

The Hybrid technique combines both Content-Based and Collaborative Filtering methods.

When applying these recommendation techniques, a similarity method is required to calculate the degree of similarity between two items.

Various similarity methods can be used, including:

  • Euclidean distance
  • Manhattan distance
  • Minkowski distance
  • Cosine similarity
  • Jaccard similarity

Evaluation

Once the recommender system is capable of suggesting items to users, the next step is to assess the accuracy of these recommendations.

Validation metrics are employed to gauge the accuracy of recommendation systems. There are two main categories of accuracy validation metrics

  • Predictive Accuracy Metrics
    • Mean Absolute Error (MAE)
    • Root Mean Square Error (RMSE)
  • Decision-Support Accuracy Metrics
    • Precision
    • Recall
    • F1-Measure (F1 Score)

Result & Conclusion

Finally, you need to evaluate and compare the recommendation accuracy of your proposed framework against the baseline from your experiment. This comparison will determine whether your proposed framework offers an improvement over the baseline.

Analyzing these results will provide valuable insights into the effectiveness of your framework and guide further advancements in this area.

Consider documenting any observed improvements or shortcomings to refine your approach and enhance the system's performance in future iterations.