I asked Gemini 1.5 Pro with Deep Research About How to Choose the Best URL from a List

6 min readDec 17, 2024

An Instinctive Algorithm for Choosing the Best URL from a List

Can you think about a instinctive algorithm that chooses the best URLs from a list of URLs? There are contextual clues like position of the URL, the anchor text and so on. Give this talk to a post-graduate level Professor.

A. (from Gemini 1.5 Pro with Deep Research)

Good morning, Professor. I’m here today to discuss an instinctive algorithm for choosing the best URL from a list of URLs. This algorithm utilizes contextual clues like the position of the URL, the anchor text, and other factors to identify the most relevant and useful link for a user.

What Makes a URL the “Best”?

Before delving into the algorithm, let’s first establish what constitutes the “best” URL in a given list. In this context, the “best” URL is the one that is most likely to satisfy the user’s information needs and provide a positive user experience. This means the URL should direct the user to a page that is:

Relevant: The content should align with the user’s current needs and interests.
Credible: The source of the information should be trustworthy and authoritative.
Useful: The page should offer valuable information or resources that fulfill the user’s objective.
User-friendly: The page should offer a positive user experience, with factors like fast loading speed, mobile-friendliness, and clear information architecture contributing to its overall quality. This ensures that users can easily access and engage with the information they seek1.

Contextual Clues for Determining the Best URL

A variety of contextual clues can help determine the best URL in a list. These clues provide valuable insights into the nature and quality of the linked content. Some of the key clues include:

Position of the URL in the list: URLs appearing higher in a list often have greater relevance to the user’s query. This observation is based on the tendency of search engines and content aggregators to prioritize the most relevant results at the top of their lists.
Anchor text: The anchor text, the visible, clickable text of a hyperlink, plays a crucial role in understanding the context and content of the linked page. Search engines use anchor text to understand the topic of the target page, and users rely on it to gauge the relevance of the link to their needs2.
Domain name: The domain name itself can offer clues about the credibility and authority of a website. Factors like the domain age (older domains often have more established reputations), domain authority (a metric used to assess a website’s overall authority in its field), and the presence of security features like HTTPS (indicating a secure connection) all contribute to a domain’s credibility3.
URL length: Shorter URLs are generally preferred as they are easier to remember, share, and read. Concise URLs also tend to be more user-friendly and less intimidating.
Presence of keywords in the URL: Including relevant keywords in the URL can signal to both users and search engines the topic and relevance of the linked page.
Link building through guest blogging: The quality and quantity of backlinks to a website can significantly influence its authority and search engine rankings. Guest blogging, where an author contributes content to another website with a link back to their own site, is an effective link-building strategy that can enhance a website’s credibility and visibility2.

An Instinctive Algorithm

Now, let’s explore an instinctive algorithm that leverages these contextual clues to identify the best URL from a list. This algorithm employs a weighted scoring system, assigning a numerical score to each URL based on the presence and strength of the contextual clues. The URL with the highest score is deemed the most suitable choice.

The weighted scoring system offers several advantages:

Objective Evaluation: It allows for a more objective assessment of URLs by minimizing personal biases and preferences.
Nuanced Prioritization: By assigning different weights to various criteria, the algorithm can prioritize the most important factors in URL selection.
Flexibility: The weights can be adjusted to align with specific user needs or contexts, making the algorithm adaptable to different situations4.

Here’s a possible weighting scheme for the different contextual clues:

Contextual Clue

Weight

Position in the list

30%

Anchor text

25%

Domain name

20%

URL length

15%

Presence of keywords in the URL

10%

The algorithm operates as follows:

Analyze the URL: Extract the relevant contextual clues from each URL in the list. This involves identifying the position of the URL, analyzing the anchor text, assessing the domain name, measuring the URL length, and checking for the presence of keywords.
Assign scores: Each URL is assigned a score for each contextual clue based on predefined criteria. For instance, the first URL in a list might receive a higher score for position, while a URL with highly relevant anchor text would score higher in that category. The scoring criteria should be designed to reflect the relative importance of each clue in determining the overall quality of the URL.
Calculate the weighted score: The score for each clue is multiplied by its corresponding weight. For example, if a URL receives a score of 8 out of 10 for anchor text, and anchor text has a weight of 25%, the weighted score for anchor text would be 2 (8 x 0.25 = 2).
Calculate the total score: Sum the weighted scores for each contextual clue to obtain a total score for each URL.
Choose the best URL: The URL with the highest total score is selected as the best option.

To illustrate, consider a scenario where a user searches for “best coffee shops in Seattle.” The algorithm would analyze the URLs in the search results, considering factors like the position of the URL, the relevance of the anchor text to the search query (e.g., “Seattle’s top-rated coffee shops”), the domain name (e.g., a well-known travel website vs. a personal blog), the URL length, and the presence of keywords like “coffee” and “Seattle” in the URL. By applying the weighted scoring system, the algorithm would identify the URL that best matches the user’s search intent and offers the most credible and useful information.

Limitations and Future Research Directions

While this instinctive algorithm provides a promising approach to URL selection, it does have limitations:

Inability to account for all contextual clues: The algorithm may not capture the full spectrum of contextual information available. For example, it might not consider the user’s past browsing history, the user’s current location, or the specific device being used.
Difficulty adapting to changes in URL usage: The algorithm may struggle to keep pace with evolving trends in URL structure and usage. For instance, the significance of keywords in URLs might diminish as search engines become more sophisticated in understanding user intent.
Exact Match Domains: The algorithm might need refinement to account for “exact match domains,” where the domain name perfectly matches the user’s search query. While such domains might seem highly relevant, they don’t always guarantee quality content or a positive user experience5.

Future research could address these limitations by:

Developing more sophisticated algorithms: Explore machine learning techniques and natural language processing to create algorithms that can analyze a wider range of contextual clues and adapt to changes in URL usage patterns.
Incorporating user feedback: Gather user feedback on the relevance and usefulness of selected URLs to refine the algorithm’s scoring criteria and improve its accuracy.
Evaluating the algorithm’s effectiveness: Conduct real-world studies to assess the algorithm’s performance in different contexts and user scenarios.

Conclusion

This instinctive algorithm presents a novel approach to URL selection, leveraging contextual clues and a weighted scoring system to identify the most relevant and useful links for users. While it has limitations, further research and development can enhance its capabilities and address its shortcomings. This algorithm has the potential to significantly improve information retrieval, content recommendation, and the overall online experience by helping users navigate the vast landscape of the internet more effectively. By understanding and utilizing the wealth of information embedded in URLs and their surrounding context, we can create more intelligent systems that connect users with the information they need in a seamless and intuitive manner.

Works cited

1. Are Contextual Links A Google Ranking Factor? — Search Engine Journal, accessed December 17, 2024, https://www.searchenginejournal.com/ranking-factors/contextual-links/

2. Contextual Relevance of Links: Why Does It Matter? — Linkflow, accessed December 17, 2024, https://linkflow.ai/blog/contextual-links-seo/

3. Selecting Great URLs | UMC — Michigan Technological University, accessed December 17, 2024, https://www.mtu.edu/umc/services/websites/urls/

4. Weighted Scoring Model: What It is & How to Create It — Userpilot, accessed December 17, 2024, https://userpilot.com/blog/weighted-scoring-model/

5. Using an oddly specific URL — Advantageous or not? : r/bigseo — Reddit, accessed December 17, 2024, https://www.reddit.com/r/bigseo/comments/ppq0we/using_an_oddly_specific_url_advantageous_or_not/